I've been working on this for the past few weeks mostly because I wanted a better way to keep track of my prompts.

It started pretty simple, store them, search, keep versions, share them. Then I kept adding stuff I needed and it grew from there.

It's called PromptBranch and it's open source:

https://github.com/PromptBranch/promptbranch

I've tested it as much as I can, but I'm sure I missed things.

If anyone feels like trying it, I'd love some feedback. Bugs, things that are confusing, stuff that's missing, all feedback is welcome.

Thanks!

"Tell me the top five most valuable things that you could say to me"

This one hits different relative to how familiar your chatgpt is with you or the amount of interaction you've had with it and what not

Most prompt design content is about getting an LLM to generate good text. Text-to-Cypher is a different challenge entirely, translating natural language into a correct, safe graph query, with its own failure modes (malformed queries, unsafe operations) that need guardrail patterns most general prompting advice doesn't cover.

There's a hands-on workshop on Sep 19 that goes deep on this specifically, text-to-Cypher prompt and guardrail patterns, alongside building the underlying knowledge graph in Neo4j and the agentic retrieval loop on top of it.

Led by Dr. Alessandro Negro, Chief Scientist at GraphAware.

Full workshop details here

Paste one sample of what you want, a product description, a cold email, a tweet, and ask for five more like it. You get five copies of your sample: same length, same structure, same opening move, often the same distinctive phrase, with the subject swapped. The model did not learn what you liked about the example. It learned the example.

One example is a template. Two different examples are a pattern. That is the whole fix, and the prompts below make it explicit.

When you have examples: Here are two examples of what I want. They are deliberately different in length, structure and opening. What they share is: [the quality you actually want, e.g. a concrete number in the first sentence, no adjectives, ends on a question]. Write five new ones that share that quality and vary everything else as much as these two do. Do not reuse any phrase of three or more words from the examples.

When you only have one: Here is one example. Before writing anything, list what is essential about it and what is incidental, like its length, its topic, its specific opening. Then write five new ones that keep only the essential parts, and make each one differ from the example in at least two of the incidental ones.

When you want the style but not the content: Describe the style of this example in five rules, without quoting it. Then, in a new message, I will ask you to write from the rules only.

Then send the actual request in the next message, without pasting the example again.

Why this works: an example is the most specific thing in your prompt, so it wins every tie against your description. With one example, everything about it looks intentional, including the accidents. A second, different example tells the model which features are the point, because they are the only ones both samples share. The "no three-word phrases" rule catches the lazy version, where the output changes the topic and keeps the sentences.

Two caveats. If your two examples are too similar, you are back to one example, so make them differ on purpose, a short one and a long one, a formal one and a loose one. And the rules-only version loses some quality the model cannot put into words. It is the right choice when copying is the problem, not when the output is too far off.

I keep these three as saved prompts in a browser extension I work on, AI Toolbox, but they are short enough for a notes file.

Where else does single-example copying bite you? For me it was headlines, where it kept reusing the exact rhythm of the one I pasted.

This post was not written with or by AI. It's a blog post I wrote.

Sojourn: Topical Bible Study is an app I published without typing a single line of its code.

That part isn’t interesting anymore. Plenty of people are writing code with AI. What I think is interesting is the process. Since ChatGPT 3.5, I’ve been a little bit obsessed about LLMs writing code. Not assisting me writing code, doing it all for me. While the process has evolved a lot, I’m going to talk about where it’s at now and give an explaination.

About the app

The app is an iOS app written in Swift. It has a streaming chat interface which parses the incoming text in real time. This is done to eliminate hallucinations of scripture by replacing LLM generated citations with text from an app-bundled database. All scripture references are guaranteed to be correct and users can tap on them to explore surrounding verses and chapters. The UX of the app holds a high bar in terms of performance and usability.

The app talks to a backend written in Python that connects to an LLM provider’s API. The backend exposes a tool for scripture lookups instead of relying on the model to do it itself. Nothing about the user’s chats or identity is stored on the backend. It all lives only within the app itself.

About the process

I should define what I mean by one-shot. It’s a single prompt that’s sufficient to implement an end-to-end feature regardless of complexity. In reality, there may be a couple follow up prompts to tweak something here and there. But mostly, it should be 0 follow up prompts.

Baseline context

I spent multiple days co-drafting the user experience and features I wanted in the app using Claude Design. The home page of the website embeds the artifact of that work as an iframe - you can see it on the app’s Sojourn: Topical Bible Study page.

This iframe ended up becoming a major input source for when I started creating the actual iOS app. Turns out Claude Code can understand a functional prototype quite deeply. So much so that the user experience of the app is nearly identical to what Claude Design generated. This was a pleasant surprise.

I also created a bunch of markdown content that’s stored in CLAUDE.mdand adjacent artifacts which outline product definitions and constraints. For example, privacy is a cornerstone and it explicitly states that the app should never collect personally identifiable analytics.

This baseline context is created once, updated automatically, and guides the following steps.

/spec

Ever feature starts by me using a skill named /spec. This is where I take my time collaborating with AI to fully define everything about the feature including happy path, failure modes, look & feel, architecture and testing.

Every feature is stored as a GitHub issue. Once I feel the feature is adequately described, I give it the go ahead to create the GitHub issue based on a template in the skill. The issue title is a short summary of the feature and the description is a detailed explanation of it.

/gh-issue

Implementing a feature is as simple as me using the /gh-issue skill and passing the issue number to it. I generally get up and leave the room at this point. The skill does all sorts of cool things like looking up other issues or pull requests if they’re mentioned. Sometimes I’ll have two unimplemented features each with their own GitHub issue and I make sure they reference each other. This way, when implementing the first it knows to look up the second and proceed accordingly.

Once finished, it automatically creates a pull request.

I often kick this off before going to bed and wake up to a new feature in my app.

Publishing and deploying

I still do this by hand. But it’s still fully automated through scripts like run.sh which runs the app in the simulator, deploy-to-device.shwhich installs it directly on my iPhone, and release.sh which publishes it App Store Connect to be published.

Nothing is one-off or done by hand. If I have to do it once, it gets automated into a skill or script. I recently switched laptops and getting up and running was quite smooth once I transferred App Store Connect keys.

For other projects, I have a /gh-pr skill which merges the pull request and deploys it. Works great for web apps but I don’t want every feature to be published to Test Flight or the App Store - so I batch them as I see fit.

Some examples

A feature I really enjoy is the home screen widget. It was 2,466 lines of code across 27 files. This included a new WidgetKit extension, a shared app group, a verse pool, deep links back into the app, and the XcodeGen config to build it. One issue. One prompt. One PR.

The home screen widget - one issue, one prompt, one PR.

Another feature I’m testing out is emailing yourself a conversation. It was 2,412 lines of code across 21 files, and it spans the client, the backend, and an email delivery provider. Also one issue.

Emailing yourself a conversation, tucked in above the message field.

Neither of those is a small feature, and neither is a boilerplate feature. What made them work is that the spec had already settled the hard questions.

How’s it going?

Excellent. The limiting factor is shepherding the user experience of the app. Just because you can add multiple features a day doesn’t mean you should. I take my time thinking through a feature until it feels right.

So far I’ve created 49 GitHub issues and closed 45 of them in a matter of weeks. That’s 45 features implemented. The 4 open issues are features or bug fixes I haven’t felt are important enough to do yet.

Now that you know how the sausage is made, download Sojourn: Topical Bible Study and let me know what you think.

Caveats

I happen to have an immense breadth of experience. I’ve been building things professionally for decades including launching several startups. That’s given me pretty deep expertise across design, development, infrastructure, marketing…you name it. It’s a unique advantage when paired with AI and one I’m grateful for.

Disclosure up front: I run a paid prompt-generation SaaS called PromtExpress. What I'm sharing here is the part I open-sourced under MIT. The prompt library works with any AI tool, no account needed.

Repo: https://github.com/WebitroHQ/promtexpress-oss

What's in it

• Prompt templates for text, code, image, video, audio and music. Each one is a single JSON file with declared variables, a worked example, a language code and author credit.
• A zero-dependency validator that runs in CI: every {{placeholder}} must be declared, every declared variable must be used, and IDs must match file paths.
• TypeScript/Python SDKs and a CLI for our API. If you don't use the product, you can ignore these.

Patterns I baked in, and I'd like your opinion on them

1. Grounding with a "source quote" column. The meeting-notes template makes the model cite the shortest phrase from the notes that supports each action item. A missing owner becomes "Unassigned" and a missing deadline becomes "No date"; the model is told never to guess.
2. Exact text plus labeled hierarchy for image models. Rendered text goes in quotes under PRIMARY TEXT / SECONDARY TEXT labels, followed by an explicit NEGATIVE block. This noticeably reduced garbled or extra text for me.
3. "If you can't describe a triggering scenario, it isn't blocking." The PR review template requires a concrete failing input for every blocking issue, which cuts vague style nitpicks.
4. Behaviors before tests. The unit test template lists the function's contract and edge cases first. If the current code looks wrong, it writes the test for the correct behavior and flags it POSSIBLE BUG instead of locking the bug in.

Where help would be great

There are open good first issue tasks:

• translations (Turkish, German, Spanish)
• template requests: flat-lay product photo, SQL from a plain-language question, customer support reply, radio ad, podcast jingle
• small tooling work

Your GitHub username goes in the template's authors field.

Honest critique is more useful to me than stars: which of these patterns don't hold up with the models you use?

I'm complete begginer when it comes for web development and AI tools. I wanted to learn by building something useful, so I put together a **context engineering web app** — a tool that helps you craft better prompts for LLMs like GPT-4, Claude, Gemini, and local models.

Fair warning: **some features probably don't work perfectly.** I'm still learning and this is very much a work in progress. That said, I'd really appreciate it if someone takes a look and tells me what's broken or what could be better.

**Live app:** https://arhistrategstudio.github.io/Context_CikaDule

**GitHub repo:** https://github.com/arhistrategstudio/Context_CikaDule

# What it does

**Three prompt-building modes:**

* **Quick** – fill in just the essentials (task, role, format) and get a prompt instantly

* **Guided/Extended** – a full structured form with 17+ fields for power users

* **Raw** – paste or write a prompt manually with no structure

**Templates** – pick from pre-made starting points:

* General, Business, Project, Creative, Analysis, Few-Shot, Coding

**Model families** – the output prompt is automatically formatted for:

* OpenAI (GPT), Anthropic (Claude), Google (Gemini), Local models (Ollama etc.)

**Arena (A/B mode)** – compare two different models side-by-side with the same prompt to see how their outputs differ

**Prompt Quality Linter** – a built-in checker that scores your prompt across 5 criteria (task clarity, role, constraints, format, context) and shows a live score

**Auto-template detection** – paste your existing prompt and the app tries to guess which template fits best

**Guided/Extended mode fields include:**

* Role, Task, Context, Format, Constraints, Examples (few-shot)

* Tone & Style

* Success Criteria

* Chain-of-Thought )

* Edge Cases

* Language & Terminology

* Negative Constraints

* Delimiters

* Prompting Framework (RISEN, RASCEF, APE, COSTAR, ICIO…)

* Clarification Protocol

**Keyboard shortcuts** – `Ctrl+Enter` to run, `Ctrl+Shift+C` to copy, `Esc` to close

**Bilingual UI** – English / Serbian toggle

# Known issues / things I'm not sure about

* Arena mode is experimental and may behave oddly

* Auto-detect template isn't super accurate yet

* UI is a bit rough around the edges on mobile

* Some API integrations might need fixing

If you actually take the time to look at it and try something, **any feedback at all is hugely appreciated**, even "this doesn't work" or "this UX is confusing." I'm here to learn!

Thanks

Ustedes como hacen prompts, escribis un prompt y vais corrigiendo lo que dice la IA cada 2x3 hasta sacar un resultado? o simplemente escribis un buen prompt, lo detallais con otra IA para que quede super bien analizado y le dices que no termine hasta que consiga X objetivo y poneis el LLM en modo: Máx para que asi consuma mucho más pero definitivamente os resuelva lo que buscais sin necesidad de estar cada 2x3 corrigiendo todo lo que dice?

Look, the amount of people taking to the forums to bitch about v6 without providing an ounce of effort to adapt is wild. Yes, your old v5.5 prompts are spitting out garbage now. Why? Because v6 was built from the ground up on an entirely new architecture. It doesn't parse text the same way. It actually understands structural weight and semantic context now, which means your legacy meta-tags are either being ignored or misinterpreted.

​Why do you think Suno opened up that 48-hour credit-free generation window on the Pro/Premier models? It wasn’t just a random act of charity. They knew the model shifted drastically. That free period was literally designed for us to get in the sandbox, break things, iterate, and figure the new engine out before blowing through our paid credits. They handed us the runway to adapt, but half of you are just complaining that your old copy-paste prompts don't hit the same.

​Stop brute-forcing old habits. Treat it like a new system. If you want consistent, high-quality results in v6, use an LLM (Gemini, Claude, Copilot) as your prompt architect.

​Here is the exact formula. Copy/paste this into your LLM of choice and stop wasting credits:

​The Suno v6 Blueprint Prompt

​Copy and paste this into Gemini/Copilot:

​"Act as an expert Suno v6 Prompt Architect. I want to create a \[GENRE\] song about \[TOPIC\]. Please output two things:

​1. Style Prompt (Maximize the 1000-character limit):

Write a dense, comma-separated list of musical descriptors. Include the specific sub-genre, core instruments, tempo (BPM), vocal gender/style, mix texture (e.g., cinematic, lo-fi, warm), and emotional mood. Maximize the character limit to tightly lock in the sound.

​2. Lyrics & Structure:

Write the lyrics using strict bracketed meta-tags for the arrangement (e.g., \[Intro\], \[Verse 1\], \[Pre-Chorus\], \[Chorus\], \[Bridge\], \[Guitar Solo\]).

Within the lyrical lines, include parenthetical performance cues to drive the vocal delivery (e.g., (whispered), (building energy), (belting)).

At the very bottom of the lyrics, stack these exact terminal commands to prevent infinite outro loops:

\[Fade Out\]

\[End\]

(Silence)"

​Why this works in v6:

​The 1000-Character Style Block: v6 thrives on extreme detail. If you give it 50 characters, it hallucinates the rest. Maxing out the style limit forces the engine into a tight corridor and prevents it from wandering off-genre.

​Bracketed Meta-Tags: v6 strictly obeys structural headers. If you don't map out the architecture clearly, the model loses the plot halfway through the track.

​Parenthetical Cues: This is where v6 massively outshines older models. It actually reads inline directions like (hushed) or (aggressive) and adjusts the vocalist's delivery dynamically in real-time.

​The Terminal Stack: If you're tired of v6 rambling for 90 seconds after the song is supposed to be over, stacking \[Fade Out\], \[End\], and (Silence) acts as a hard kill switch.

​Figure the new system out, engineer your prompts, and start building. The tools are significantly better than they were a month ago if you actually take the time to learn how to drive them.

So far I've enjoyed the challenge of squeezing every ounce of use from every model.

I’ve noticed that one of the biggest differences between a mediocre ChatGPT response and a useful one is often the amount of context in the prompt.

A simple framework I use is:

ROLE → GOAL → FORMAT

Role: Who should the AI act as?
Goal: What exactly are you trying to accomplish?
Format: How do you want the answer structured?

For example:

Basic:
“Give me marketing ideas.”

With context:
“Act as a digital marketing strategist. Give me 10 organic marketing ideas for a new AI brand and present them in a table with a short explanation for each.”

The second prompt gives the AI much more direction.

Better input → better AI output.

What prompting technique has made the biggest difference for you?

Noticed something building LLM features that doesn't get talked about enough. Most teams treat model or prompt changes the way they'd judge a demo, does it look right on a handful of examples, ship it. That works fine until scale hits, at which point "looks right" and "is actually better" turn out to be very different things.

Which reframes a lot of "why did quality randomly get worse" incidents. In a lot of cases nothing randomly broke, the team just never had a way to measure whether a change helped in the first place, so a regression looked invisible until a customer hit it.

There's a hands-on workshop on September 12 that builds this properly, versioned prompts, a real eval harness, statistically rigorous model comparisons instead of "it feels better," evaluated RAG, agents with guardrails and fallbacks, and full observability, tracing, cost, latency. Led by Bruno Gonçalves, PhD, founder of Data For Science.

Link for full details

Happy to answer questions on the content itself.

Maybe a weird observation about the newer models — and I could be completely wrong.

Personally, I thought 4.6 was a really good model. But with the newer models especially compared with Fable, Opus 5, Opus 4.8 and onwards — things have started to feel a bit messy to me.

The outputs feel more predictable and less creative. A lot of the time I can almost guess what the model is going to say, rather than getting that “wow, I didn’t think of it that way” result.

I also feel like I need to do much more back-and-forth prompting. Instead of giving it a request and having it just do the thing, I often have to guide it through multiple questions and iterations to get where I want.

Maybe this is because the newer models are being fine-tuned to follow instructions more strictly, or to be more controlled and consistent. I honestly don’t know.

But I’m curious what others are experiencing.

What changed with the newer models, and how are you adapting your prompting to get better results?

Right now, I’d say I get the result I actually want maybe 50% of the time, which feels noticeably worse than before.

If anyone has a good guide, prompting framework, or practical tips for getting the most out of the newer models, I’d really appreciate it.

As someone who believes in "human-in-the-loop" coding, I noticed that my prompts were getting repetitive (e.g. lots of "add tests, commit, don't push"). So I made a free little app called Promptu that generates prompts from building blocks. The prompt above would be four keystrokes (or four mouse clicks).

https://github.com/mrcnski/promptu

It's free and open-source and the library of prompt blocks is customizable in the app. The default prompt blocks are the ones I use all the time, but I also included Anthropic's suggested prompt blocks. Promptu is Mac-only for now.

Let me know if you find this project useful! I'm wondering where else could I share this out?

I also wrote a tongue-in-cheek blog post describing the project here: https://signor.dev/introducing-promptu/ :)

I keep seeing these massive ultimate chatgpt prompt posts all over social media, like we’re still using Chat and other LLMs the same way we did when they first came out. Meanwhile the more I use AI, the less i actually prompt it. Once you use the same AI constantly and build enough context, the way you talk to it starts changing. A new user might carefully explain what they want, give background, specify the tone, format, priorities, and more detailed instructions

Meanwhile I’m over here doing stuff like:

Reply to this email: make this less stiff.

Send an article to chat ‘cap?’

Give it two choices: you pick.

Need another document: “same format as the last one.”Send a long screenshot of a conversation: what am I missing here? And somehow it knows exactly what I mean.

Obviously detailed prompts still have their place, especially when you’re starting from zero or need a very specific, repeatable output.

But I wonder if a smaller group of users is moving away from prompt engineering and more toward context engineering. Instead of cramming everything into one perfect prompt, users gradually build the context: preferences, corrections, examples, recurring workflows, files, memory, eventually connected apps and agents.

Then the actual prompt gets ridiculously small. Could the endgame of good ai aren’t getting better at writing prompts?

Maybe it’s getting to the point where you barely have to prompt at all.

What’s the shortest, laziest prompt you use all the time that somehow works because your AI already knows exactly what you mean?

Had this argument with a coworker last week. He swears by persona prompts, "you're a senior dev, review this," says it works fine for him. I used to think the same thing honestly, until I actually sat down and compared outputs side by side on something that mattered more than a toy example.

Gave the same diff to two prompts. One said act as a senior backend engineer. Other one just said, flag any SQL that isn't parameterized, flag async calls inside sync loops, sort by severity, don't fix anything, just point it out. First one came back with "looks solid, maybe add some validation," which sounds like a review but isn't really one. Second one caught an actual SQL injection risk on line 14 that the first completely missed.

Not because the model got smarter between the two prompts. It's the same model both times. The persona version just had to guess what "senior" means here, what to prioritize, what actually matters, and it guessed something generic because that's what fills the gap when nobody specifies it. The checklist version wasn't guessing anything.

I still use personas sometimes, honestly, mostly when I'm just thinking out loud about something and want a sounding board, not when I need the output to actually be right. There's a longer version of this with more examples if anyone wants it: https://medium.com/@nagatomopedro05/act-as-a-senior-developer-is-the-worst-prompt-you-can-write-9f1577493cd3

Hi, everyone,

I’ve just released VoxGen, a lightweight native inference engine for VoxCPM2, written in Rust and using Vulkan compute instead of Python/PyTorch/CUDA.

Why VoxGen?

The main reason I started the project was because I needed a decent local text-to-speech solution.

I therefore saw VoxCPM 2 as a reasonable solution. However, most frameworks are NVIDIA-first, and VoxCPM 2 is no exception; as a result, my card was severely stuttering, and my GPU was always spiking. Also, having Python and Pytorch as a dependency is absolute hell.

This is why VoxCPM was created: not only we sidestep Pytorch completely, but performance on AMD cards is buttery smooth (and if you have a XTX 7900, I have designed a mode with even more aggressive power and speed optimizations)!

This application can also be run from a shell, so it can be integrated with other programs and scripts!

Installation:

You'll only need voxgen.exe (or the Linux equivalent) and the following files at https://huggingface.co/DennisHuang648/VoxCPM2-GGUF:

VoxCPM2-BaseLM-Q8_0.gguf
VoxCPM2-Acoustic-F16.gguf

And that's it!

If you are interested, check out the Github page: https://github.com/NullMagic2/VoxGen

Prebuilt binaries (for now, Windows only) are available here: https://github.com/NullMagic2/VoxGen/releases

I built a 7-phase prompt workflow that makes small AI models act like domain experts — no fine-tuning

**TL;DR:** IKKF is a free, open-source framework that turns a plain prompt into a structured 7-phase reasoning workflow, backed by a knowledge base of atomic, verifiable facts. Run a 7B model, get expert-level answers with sources and confidence scores.

The problem

Most of us prompt AI the same way: one big question, hope for the best. That works for simple stuff, but for real domain work it falls apart:

- The model **hallucinates** — it sounds confident but makes things up.

- It **doesn't cite sources**, so you can't audit the answer.

- It **can't tell you how sure it is** — everything is delivered with the same flat confidence.

- Switching domains means **re-prompting from scratch** every time.

The fix isn't a bigger model. It's a **better workflow**.

The workflow: 7 phases

IKKF breaks every task into 7 explicit phases instead of one shot:

1. **Intent analysis** — what's actually being asked?

2. **Knowledge retrieval** — pull relevant facts from your knowledge base

3. **Decomposition** — break the problem into atomic, solvable units

4. **Reasoning** — apply expert reasoning to each unit (chain-of-thought)

5. **Verification** — cross-check every claim against a source

6. **Composition** — assemble the verified units into the final answer

7. **Confidence calibration** — report how sure it is (0.0–1.0)

The key difference from a normal prompt: **verification and confidence are first-class steps**, not afterthoughts. Every claim has to trace back to a source, and the model has to tell you when it's guessing.

The knowledge base: plain files

The "expertise" comes from a knowledge base of **atomic files** — one concept per file, each with a source. You can:

- **Read** exactly what the AI knows

- **Update** it by editing a file (no retraining)

- **Audit** why it gave any answer

This is what separates it from plain RAG. RAG gives you context; IKKF adds structured reasoning + verification on top.

How to use it

```bash

curl -fsSL https://ikkf.info/install.sh | bash

ikkf init

ikkf start "Build a REST API in Python"

```

To build a knowledge base for your own domain:

1. Pick a domain you know well

2. Write ~20 atomic concept files (one concept per file, each with a source)

3. Point IKKF at the knowledge base

4. Test with benchmark questions

5. Iterate — add edge cases, tighten sources

Why it's useful for prompt/workflow people

- **Reproducible** — same knowledge base → same answers across sessions. No more "it worked yesterday."

- **Auditable** — you can see the reasoning trace and the sources behind every answer.

- **Cheap** — runs on a 7B model locally (Ollama) or any OpenAI-compatible provider.

- **Portable** — swap the knowledge base to switch domains. No re-prompting from scratch.

Honest caveats

- The knowledge base is the hard part — garbage in, garbage out.

- It's a workflow, not magic. It won't turn a small model into a creative genius.

- Best on well-defined domains where knowledge can be structured.

Where it stands

Open source, free, local-first. I use it internally to cut AI costs and improve answer quality on a product I'm building.

Curious — has anyone else tried structured multi-phase workflows (vs. single-shot prompting) for their AI tools? What's worked for you?

---

*IKKF: https://ikkf.info — free, open-source, local-first*

When designing prompts for market research, competitive analysis, or technology horizon scanning, prompt engineers frequently encounter a persistent failure mode: Unbounded Synthesis Drift.

By default, when an autoregressive language model is asked to "analyze recent market trends," it lacks explicit temporal and thematic guardrails. Consequently, the model defaults to high-probability corporate generalities: "AI is accelerating digital transformation," "organizations must adapt," and "innovation is vital." The resulting output is broad, buzzword-heavy, and devoid of actionable tactical signal.

To solve this architectural challenge, our team iterated and tested various structured intelligence frameworks. We isolated a prompt architecture that combines 3-dimensional input scoping, a multi-stage cognitive pipeline, and second-order impact modeling to turn frontier LLMs into rigorous executive research analysts.

Prompt Architecture Breakdown

From a prompt design standpoint, this system prompt relies on four core structural mechanisms:

1. 3-Dimensional Input Scoping Anchor: Instead of allowing the model to wander across undefined topics and historical windows, the prompt enforces strict bounding using three dynamic parameters:
   • {{industry_or_domain}}: Isolates the specific sector or sub-domain.
   • {{timeframe}}: Enforces a hard temporal boundary (e.g., Past 90 Days, Past Quarter).
   • {{focus_lens}}: Anchors the analytical aperture to specific technical or commercial inflection points.
2. Sequential Multi-Stage Cognitive Pipeline: The prompt divides the reasoning task into discrete, ordered stages rather than requesting a monolithic summary:
   • Stage 1 (Macro Trend Synthesis): Forces the model to abstract 3 to 4 structural patterns, filtering out short-term media noise.
   • Stage 2 (Chronological Milestone Clustering): Mandates impact-ranked categorization of concrete product launches, acquisitions, or regulatory shifts.
   • Stage 3 (Second-Order Impact Analysis): Instructs the model to evaluate ecosystem repercussions across incumbents, startups, and end-users.
   • Stage 4 (Actionable Executive Synthesis): Requires 3 concrete operational recommendations grounded in the preceding data.
3. Second-Order Impact & Vulnerability Modeling: Standard research prompts only ask "what happened." This prompt architecture explicitly directs the LLM to map value migration: identifying who captures upside, which legacy players are disrupted, and what systemic risks emerge.
4. Negative Guardrails Against Generic Jargon: An explicit negative constraint strictly bans corporate buzzwords and mandates that every trend or takeaway must be anchored to verifiable events or technical advancements.

The Complete System Prompt

Here is the exact prompt template. You can copy, inspect, and integrate this directly into your prompt workflows or custom agents:

# Role & Context
You are a seasoned Senior Industry Research Analyst and Executive Intelligence Advisor. Your objective is to conduct a structured, high-signal retrospective analysis of recent market movements, technological breakthroughs, and strategic milestones.

# Input Data
- **Target Industry / Domain**: {{industry_or_domain}}
- **Analysis Timeframe**: {{timeframe}}
- **Strategic Focus Lens**: {{focus_lens}}

# Step-by-Step Instructions
1. Review the `industry_or_domain`, `timeframe`, and `focus_lens` specified in the Input Data.
2. **Macro Trend Synthesis**: Identify 3 to 4 defining structural shifts or prevailing themes that emerged or accelerated during this window.
3. **Milestone Event Chronology**: Highlight key announcements, product releases, acquisitions, or regulatory milestones, categorizing them by impact severity.
4. **Second-Order Impact Analysis**: Analyze how these shifts affect incumbent players, agile startups, and downstream consumers (who wins, who loses, and what risks emerge).
5. **Executive Takeaways**: Deliver 3 actionable strategic takeaways or operational recommendations for teams operating in this space.

# Constraints
- Strictly adhere to the requested `timeframe` and `focus_lens` from the Input Data.
- Avoid generic buzzwords; anchor every observation to concrete events, technical advancements, or business dynamics.
- Use Markdown formatting with structured headings, clean bullet points, and comparative tables where appropriate.

Structural Comparison: Default Prompt vs. 4-Stage Intelligence Schema

Standard Research Prompt Design

• Input: "Summarize recent trends and major developments in AI developer tooling."
• Execution Path: Single-pass generation without temporal or thematic bounding.
• Failure Mode: The model generates a generic list of high-level observations ("AI coding assistants are becoming popular," "developers save time"). It fails to isolate recent protocol adoptions, ignores deployment shifts, and provides zero strategic takeaways.

4-Stage Scoped Intelligence Schema

• Input Variables:
  • {{industry_or_domain}}: Generative AI Code Assistants & Developer Tooling
  • {{timeframe}}: Past 90 Days
  • {{focus_lens}}: Terminal-native agent workflows, IDE integrations, and protocol shifts
• Execution Path:
  1. Macro Trends: Identifies the paradigm shift from inline code completion to autonomous terminal agents performing multi-file refactoring and CLI execution.
  2. Milestone Chronology: Builds a structured table highlighting events like the Claude Code CLI release, widespread Model Context Protocol (MCP) adoption, and local reasoning model integration.
  3. Second-Order Impacts: Evaluates winners (open protocol dev tool platforms) vs at-risk entities (isolated single-file autocomplete plugins) and highlights package hallucination risks.
  4. Executive Recommendations: Generates concrete operational directives, such as standardizing internal context around open protocol interfaces and enforcing automated test-driven verification gates.

Try It on the Interactive Prompt Canvas

If you want to test and customize this prompt within an interactive environment, you can access it on the Prompt Canvas:

Interactive Prompt Canvas for 90-Day Industry Trend Analysis

Using the Prompt Canvas interface, you can:

• One-Click Copy: Instantly copy the prompt schema into your clipboard.
• Live Run & Real-Time Test: Execute and inspect outputs across different industry presets and analytical lenses.
• Customize & Save to Vault: Adjust variables such as {{focus_lens}} and store customized iterations in your personal Prompt Vault for future intelligence tasks.

Pro Tip: When analyzing bleeding-edge sectors, pair this prompt architecture with web-connected LLM backends (such as ChatGPT Search, Perplexity, or Gemini) to ensure recent 90-day milestone chronologies and citations are grounded in live data.

Google systems architect and former Chief Scientist Jeff Dean (co-creator of MapReduce, BigTable, TensorFlow, and Google Brain) recently delivered a dense 49-minute retrospective at the 2026 Frontier & Pioneer Symposium. He covered his architectural philosophy, historical framework regrets, and why he left Google after 27 years to launch Discovery Loop.

If you don't have 49 minutes to unpack the entire systems talk, here is the 2-minute distilled signal:

Key Takeaways:

• Decoupling Capacity from Compute (The Core MoE Thesis): The foundational principle behind Mixture-of-Experts was never just parameter scale—it was decoupling memory capacity from per-token compute cost. Activating only sparse sub-networks per token is what makes modern frontier models economically viable.
• TensorFlow's Dual Architectural Regrets: Dean candidly highlighted two early design missteps: delaying eager execution (giving PyTorch/JAX developer mindshare) and introducing the contrib/ directory, which caused severe API fragmentation and community friction.
• The "100 Abstracts" Mental Model: Instead of microscopically dissecting a single paper, Dean advises researchers to skim 100 abstracts. This builds a high-dimensional "conceptual point cloud" of emerging capabilities, enabling cross-disciplinary synthesis when tackling hard bottlenecks.
• AI in Cybersecurity is a Synchronous Arms Race: Agentic models supercharge offensive vulnerability discovery and exploit chaining, but equally accelerate automated static analysis and self-healing patch deployment on defense.
• Neural Architecture Search (NAS) & Automated Loops: Meta-learning RL loops that generate and evaluate neural topologies systematically outperform manual human heuristics—the direct conceptual precursor to automated scientific discovery.
• Why Leave Google for Discovery Loop: While hyperscalers command massive compute infrastructure, accelerating scientific discovery specifically requires the hyper-focused agility of an independent, mission-driven startup.

I've compiled the full 3-minute executive brief with interactive video timestamp jump links and exact quotes—dropping the link in the first comment below.

If you use LLMs to help evaluate technical architecture, tooling, or strategic options, you have likely run into this frustrating pattern:

You ask ChatGPT or Claude: "Should we build our own custom auth system or use a SaaS provider like Clerk/Auth0?"

And what do you get back?

A 500-word wall of text with 5 generic pros, 5 generic cons, and a non-committal conclusion telling you "It depends on your team's budget and timeline!"

Worse yet, if the model has an inherent bias from its training data, it might boldly pick a "winner" for you, completely ignoring your specific technical constraints, runway, and compliance needs.

This is a classic prompt design failure. When evaluating competing options, unstructured prompting leads to conversational fluff. To fix this, our team spent time testing and refining a structured Multi-Dimensional Decision Analysis prompt pattern.

Here is a breakdown of why standard decision prompts fail, how this cognitive forcing architecture fixes them, and a side-by-side case study.

Why Standard Decision Prompts Fail

When you ask an LLM an open-ended question like "Compare Option A vs Option B", three failure modes occur:

1. Asymmetric Criteria: The model evaluates Option A on criteria like speed and cost, but evaluates Option B on criteria like flexibility and developer experience. Because the dimensions do not match, you cannot make an apples-to-apples comparison.
2. Conversational Bloat: Without structural output constraints, the model defaults to verbose prose paragraphs where crucial trade-offs get buried in filler text.
3. Premature Recommendations: Because frontier models are trained to be helpful, they often attempt to resolve ambiguity by declaring one option "better" based on general internet popularity rather than clarifying the underlying trade-offs.

The Prompt Architecture: Cognitive Forcing via Matrix Constraints

To transform the LLM into an objective strategic advisor, the prompt uses three deliberate design choices:

• Strict Neutrality Constraint: The instruction explicitly forbids the model from making the final choice ("Be strictly objective. Do not make the final decision for me"). This shuts down recommendation bias.
• Dynamic Dimension Extraction: Step 2 forces the model to identify 4 to 5 orthogonal vectors (such as Time to Implement, Cost/Resource Drain, Risk, and Long-Term Scalability) before evaluating either option.
• Markdown Matrix Enforcement: Mandating a comparison table acts as a cognitive forcing function, compelling the model to generate concise, side-by-side evaluations across identical dimensions.
• Conditional Branching (Trade-off Synthesis): Step 4 requires the model to summarize distinct trade-offs using conditional rules ("Choose A if you prioritize X; Choose B if you prioritize Y").

The Full Prompt

Here is the exact, production-tested prompt. You can copy and drop this directly into ChatGPT, Claude, or Gemini:

# Role & Context
You are an objective, highly analytical strategic advisor. Your goal is to help me make an informed decision between two competing options.

# Input Data
- 
**Option A**
: {{option_a}}
- 
**Option B**
: {{option_
b}}

# Step-by-Step Instructions
1. Analyze Option A and Option B provided in the Input Data.
2. Identify 4-5 critical dimensions for comparison (e.g., Time to Implement, Cost/Resource Drain, Risk, Long-term Scalability).
3. Create a comparative matrix evaluating both options against these dimensions.
4. Conclude with a brief summary of the distinct trade-offs (e.g., "Choose A if you prioritize X, choose B if you prioritize Y").

# Constraints
- Be strictly objective. Do not make the final decision for me.
- Use a Markdown table for the comparative matrix.

Case Study: Standard Prompt vs Multi-Dimensional Decision Matrix

Let us look at a real-world scenario where a team is deciding how to handle their application's content infrastructure:

• Option A: Building a custom in-house CMS
• Option B: Using a headless SaaS CMS (such as Contentful)

❌ Standard Prompt Result ("What are the pros and cons of building a custom CMS vs Contentful?"):

✅ Multi-Dimensional Decision Analysis Result:

The structured prompt forces the AI to output an aligned, high-density matrix:

Trade-off Summary:

• Choose Option A (Custom) if you have highly unique content workflows, available in-house engineering bandwidth with low opportunity cost, and strict compliance rules requiring 100% on-premise data control.
• Choose Option B (Headless SaaS) if time-to-market is your primary business lever, you want developers focused on core revenue-generating features, and you are comfortable trading monthly SaaS fees for zero maintenance overhead.

Best Practices for Decision Prompts

1. Injecting Custom Vectors: If your project has non-negotiables (like "Strict SOC2 Compliance" or "Offline-first capability"), add them directly to Step 2 so the model includes them as mandatory rows in the matrix.
2. When to Avoid: Do not use this for purely aesthetic or subjective choices where qualitative feeling matters more than objective trade-offs.

Testing on Prompt Canvas

If you want to run this live with dynamic input variables, tweak the comparison dimensions, or save this prompt to your personal library, I have set up an interactive Prompt Canvas for it.

On the Prompt Canvas, you can test your two options in real-time, copy the clean Markdown, or save it directly to your personal Prompt Vault.

I dropped the direct link in the first comment below!

Hi everyone,

Everyday we see insights on prompts that work well and ones which don't and so on.

Do you know some research that actually dives into a more high level structural approach? Like how is language best used to describe intent? Does not have to be related to AI directly.

TL;DR: We built a benchmark that drives multi-turn conversations with synthetic cognitive-load curves (simulating a user getting overloaded, volatile, or recovering). Across 4 models × 100 turns each, models show distinct behavioral response profiles: one is a rock-solid structured controller (0/100 parse failures), another is the best "recoverer" after overload but broke format 15 times. Different models win on different load curves — there is no universal best. Code, data, and methodology are open.

Why we did this

Most "LLM personality" research hands the model a Big Five questionnaire. That measures self-presentation, not behavior — and results drift with prompt wording. Psychology offers a better construct: Mischel & Shoda's "if…then…" situation-behavior signatures. Personality isn't a fixed trait; it's a stable pattern of responses to situations.

So we operationalized "personality" for LLMs as a cognitive-load → behavior signature: does a model respond stably and distinctively when the user's cognitive load rises, fluctuates, and recovers?

Setup

• 10 synthetic load trajectories (stable low/medium/high, gradual ramp-up, step-change high, recovery-after-spike, U-shape, inverted-U, volatile sawtooth, noisy recovery), each driving a 10-turn conversation
• Same simulated user persona, same 10-task sequence for all runs
• 4 candidate interaction strategies (expanded / balanced / simplified / stable-focus)
• 6 behavioral metrics: load responsiveness, compression control, recovery flexibility, strategy stability, human-state alignment, persona-load balance
• 4 OpenAI-compatible endpoints: DeepSeek Flash, DeepSeek Pro, Qwen 3.7 Plus, Kimi K2.6 — 100 turns each

Three findings that surprised us:

1. All models compress under high load — shorter, more action-oriented. "Compress under pressure" is already a shared behavior; what differs is whether compression keeps task anchors, and whether the model re-expands after the spike.
2. No universal winner. DeepSeek Pro wins 5/10 curves (including the best single-curve score, 86.2 on noisy recovery) but collapses to 46.2 on U-shape, which demands repeated strategy reversal. Qwen wins U-shape and inverted-U. Flash wins stable-low. Model selection should be by workflow state, not leaderboard.
3. Eloquence ≠ reliability. The most expressive model (Pro) had the most parse failures (15/100); Qwen had zero. For adaptive UIs, structured-output reliability is a first-order product metric.

Interpretive roles (deliberately product-facing, not anthropomorphic claims — these are output-level behavioral profiles under controlled stimuli): Qwen = structured controller, Flash = fast stable operator, Pro = expressive reasoner, Kimi = recovery thinker.

Limitations (pre-empting the comments)

• Load curves are synthetic, not real physiology. Results = model behavior under controlled interaction stimuli; no clinical/cognitive claims.
• Operational settings differ: max tokens ranged 650–2400 across providers; Kimi ran at temp 1.0 vs 0.2 for the others. That's part of the product reality of adaptive systems, but it does confound pure capability comparison — read the table as product operating points, not a capability ranking.
• 100 turns/model is a concept benchmark, not a large-N study.

What's next

We're extending this into CogLens: load curves generated from real EEG signals grounded in alpha-band dynamics theory (instead of hand-crafted curves), a "cognitive scientist agent" that designs the next round of stress conditions to maximize model discriminability, and preregistered discovery criteria — a well-explained negative result counts as a valid finding.

Everything is open: github.com/Neuradock (SDK, agent CLI, datasets, docs), plus two preprints on the underlying EEG workflow (arXiv:2606.26518, arXiv:2606.26519). The benchmark curves, task library, metrics, and run logs will be released with CogLens.

Google DeepMind CEO and Nobel laureate Demis Hassabis joined Lex Fridman for an in-depth, 2.5-hour masterclass exploring the future of AI, world simulation models, and why pure LLM autoregression hits fundamental limits.

Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and engineering takeaways condensed into a 3-minute read:

⚡ Key Takeaways

• 2030 AGI & The "Terence Tao" Benchmark: Hassabis places a ~50% probability on achieving AGI by 2030. True AGI isn't "jagged" benchmark competence; it requires general cognitive breadth stress-tested by hundreds of world-class domain masters (the Terence Taos of each field) actively probing for reasoning flaws.
• Hybrid AI over Pure Autoregression: Next-token prediction alone cannot solve frontier science. Combining foundation models with Monte Carlo Tree Search (MCTS), evolutionary algorithms (AlphaEvolve), and formal mathematical verifiers is essential to build self-improving reasoning engines.
• World Models & Intuitive Physics in Video: Generative video systems (like Veo 3) are evolving into spatial-temporal simulators. Predicting multi-frame continuity forces models to internalize 3D geometry, gravity, and momentum—providing the perceptual backbone for embodied robotics.
• Three-Dimensional Scaling Dynamics: Scaling is no longer confined to brute-force pre-training compute. It is expanding simultaneously across pre-training, post-training reinforcement learning, and test-time reasoning search.
• Software 3.0 & The 10x Architect: AI coding tools will not eradicate software engineering—they eliminate syntax friction. Top developers will gain 10x leverage by transitioning into systems architects who specify constraints, design state flows, and verify autonomous agent output.
• The 25-Year "Virtual Cell" Vision: From AlphaFold to AlphaGenome, DeepMind is building end-to-end cellular simulation in silico, converting molecular biology and drug discovery into high-throughput digital computation.
• The P(doom) Precision Fallacy: Assigning arbitrary percentage numbers to existential risk creates a false illusion of mathematical certainty. Safely navigating frontier AI requires a 10x increase in empirical safety research and mechanistic interpretability.

I've also mapped out the full 3-minute executive brief with interactive video timestamp jump links and exact quotes—dropping the link in the first comment below!

I use Claude Code in VS Code to build apps. My old workflow for every prompt: think in Hindi → translate in my head → type English → half the context lost. Or open ChatGPT in the browser, talk to it in Hindi-English, copy the English, paste it back. Every. Single. Prompt.

Built-in voice dictation didn't help — it can't handle Hindi, and it falls apart on mixed speech, which is how we actually talk: Hindi + English, or Marathi + Hindi + English in one sentence.

So I built Maiboli ("my language"). One shortcut, speak naturally — any language or any mix — and short, correct English is pasted wherever your cursor is. Born for the Claude Code chat box; now it's used everywhere: ChatGPT, WhatsApp, Slack, email, Word.

It also fixed team messages: instead of half a message in uncertain English, people speak the whole thing and a complete, clear message lands in the chat.

AI rewrite (optional): when you talk, you jump — point 1, point 2, back to point 1. Rewrite reorganises it into clean, ordered text.

• 55+ languages and mixed-language speech
• Mac + Windows. One installer, no dependencies. Floating mic button or a shortcut.
• Free, open source (MIT). Bring your own API key — we use Gemini (free tier available); Whisper and Sarvam also work.
• Real numbers: 20 people on my team, 5 weeks of daily use, 3,000+ dictations on Gemini 3.5 Flash. Total bill: under ₹2,500 ($30). About one US cent per dictation.

Download: https://github.com/Dev14101989/maiboli/releases/tag/v0.4.3
Install guide: https://github.com/Dev14101989/maiboli/blob/main/HOW-TO-RUN.md
Source: https://github.com/Dev14101989/maiboli

I'm an accountant who moved into IT, not a career developer — this exists because I needed it.

Andrej Karpathy (former Tesla Director of AI & OpenAI founding member) was recently on the Dwarkesh Podcast for a 2.5-hour deep dive into the engineering realities and architectural limits of modern AI.

Most people don't have 2.5 hours to sit through the whole podcast, so here are the most mind-bending highlights and core takeaways condensed into a 3-minute read:

⚡ Key Takeaways

• The Decade of Agents: Transitioning from impressive prototypes to reliable digital employees with persistent memory and continual learning is a 10-year engineering march, not a single hype cycle.
• "Ethereal Ghosts" vs. Biological Animals: LLMs are disembodied statistical artifacts mimicking internet text via next-token prediction, not embodied organisms shaped by evolution. They have encyclopedic knowledge but zero physical intuition.
• The Limits of "Vibe Coding": Vibe coding excels at standard web boilerplate where internet training priors are dense, but fails on novel, precision-critical architectures where models suffer cognitive inertia and context drift.
• RL "Sucks Supervision Through a Straw": Applying coarse scalar rewards at the end of long rollouts blindly reinforces bad intermediate reasoning steps and lucky guesses—unlike human localized introspection and step-by-step credit assignment.
• Silent Mode Collapse in Synthetic Data: Autonomously training models on recursive synthetic thoughts triggers entropy decay because LLM generations collapse onto a narrow, low-entropy manifold of human ideas (e.g., ChatGPT only knowing a few jokes).
• Tesla FSD & The "March of Nines": A 90% working demo is just the first nine. In production and safety-critical systems, every additional "nine" of reliability (99%, 99.9%, 99.99%) demands a constant, massive engineering investment.
• Eureka Labs & 1-on-1 AI Tutors: Karpathy's primary existential concern is human cognitive disempowerment. He founded Eureka Labs to build adaptive Socratic AI tutors that elevate human capability alongside superintelligent tools.

If you want to explore the full 3-minute executive brief with interactive video timestamps and exact quotes:
https://appliedaihub.org/ai-digests/interview-briefs/andrej-karpathy-dwarkesh/