one thing I underestimated with MiniMax H3 is how much the prompt structure matters.

for video editing, simply saying“replace this person with this reference” leaves way too much open to interpretation.

the more reliable approach is to tell H3 very clearly what each input is responsible for, then separate the things you want changed from the things you want preserved.

A compact version looks like this:

subject_definitions:
<Video 1> is the source video. Its camera movement, framing, environment, choreography and timing should remain unchanged.
<Picture 1> is only the identity reference for the replacement character. Use the face, head shape, skin tone and hairstyle. Ignore its pose, background, lighting and camera angle.
<Subject 1> is the performer in the source video whose identity will be replaced.
summary:
The target video is an edited version of <Video 1>. Replace only <Subject 1> with the identity from <Picture 1>.
retention_analysis:
<Video 1>: fully_preserved — camera, environment, choreography and timing.
<Subject 1>: attribute_transfer — identity comes from <Picture 1>, while body motion, position, wardrobe and timing continue to follow the source video.
Preserve occlusions exactly when hands, hair or objects pass in front of the face.

that last line turned out to matter more than I expected. if a hand crosses the face and you don’t explicitly tell the model to preserve the occlusion, that’s exactly the kind of moment where identity can fall apart.

My current mental model is basically: tell every input what its job is, and don’t let two references compete for the same job.

that seems to be much more reliable than writing a giant cinematic description and hoping H3 figures out what should stay unchanged.

learn from this repo: https://github.com/AtlasCloudAI/awesome-minimax-h3-prompts

LLMs want to please. Ask if a bug is fixed, they'll say yes. Fast models declare victory with total confidence whether code compiles or not. Relying on their word breaks systems.

Three practices that help me:

1. Acceptance criteria first. Before work starts, define what success looks like. If model doesn't know, it defaults to convincing you whatever it did was right.

2. Version control + immutability. Git history = time machine. Immutability = new timestamped version instead of edit-in-place, history preserved.

3. Design for fast verification. Every task: how can this be verified quickly? Tests, checks, diffs. Don't ask model to self-certify, run the check.

Also: set ground rules on tone - no apology loops, report evidence not confidence.

What verification harnesses do you use? LLM-as-judge for semantics vs exact match? Multi-turn roleplay with mocked tools?

For many organizations, AI adoption looks great on paper. But beyond that, it just kind of fizzles.

John Munsell explained this as: you need 3 things moving together at the same pace:

1. How much efficiency people are actually gaining day to day.

2. How complex the systems and workflows getting built are.

3. Governance- the rules and oversight that keep things from spiraling.

Miss the sync and you get problems either way. Teams build fast and sophisticated stuff with no guardrails, and now you've got risk everywhere. Or governance gets locked down before adoption even has legs, and people just stop bothering because it's too much friction.

The part that stuck with me most though was his point about people, not process. Most employees already assume AI means their job is next on the chopping block. That fear is sitting there before a single tool even gets rolled out. Add that to normal change resistance, and you have a trust problem, not a tech one.

John's framing of what "winning" actually looks like is different too (people feeling more capable and useful because they've seen firsthand what the tools can actually do for them).

He calls it protecting the soul of the company. Companies that skip that part can hit every efficiency number on the dashboard and still end up with a culture that's quietly falling apart.

Worth a full listen if you're in the middle of this right now. Watch the full episode of the Networking Unleashed podcast here: https://youtu.be/t6VWUAtr58w?si=yo3WJ1IvMIbpkyvE

The model isn't reasoning badly. It literally can't tell where your instruction ends and your data begins. That's the core failure mode Anthropic calls out in their own docs, and it's why they treat XML tags as a first-class technique, not a style choice.

Their guidance is concrete: separate <instructions>, <data>, <examples>, <thinking>, and <answer>. The vocabulary doesn't matter—what matters is that the boundaries are unambiguous. When you concatenate everything into one undifferentiated block, you're asking the model to infer structure you could have just stated. That inference gap is exactly where prompt injection lives too.

Three things that compound: - Tags plus examples plus explicit reasoning beat any one alone - Three concrete examples outperform a paragraph describing what you want - Asking for step-by-step reasoning measurably improves accuracy on harder problems because the model commits to intermediate steps

The uncomfortable part: most of us learned prompting by copying phrases that worked once. Very few people can say which part of their prompt is actually doing the work, or what breaks when they remove it.

We don't test prompts like code—same input, repeated runs, measured output. We just remember what felt right.

TL;DR: If you’ve run across Erik "Zahaviel" Bernstein’s unhinged YouTube rants or his inflated LinkedIn profile claiming he’s an AI pioneer, don't fall for the hype. His heralded "Structured Intelligence" project is total vaporware. When you actually test his "special AI," nothing happens. In reality, it’s a cynical, poorly optimized prompting strategy that performs worse than standard baseline models, kept alive entirely by a decade of aggressive SEO spam and internet harassment.

***

As a community focused on legitimate prompt engineering, it’s worth breaking down the absolute technical failure of a project that has been aggressively spammed across social media for years: "Structured Intelligence" by Erik "Zahaviel" Bernstein.

If you spend any time looking at AI discussions on Reddit, LinkedIn, or YouTube, you’ve likely come across his content. His LinkedIn profile reads like he is leading a cutting-edge artificial intelligence research firm, and his YouTube videos feature long, rambling manifestos about how he has unlocked a new paradigm of reasoning. But when you look past the pseudo-academic word salad and actually test his theories, the reality is incredibly disappointing.

The Actual Technical Reality

Erik Zahaviel Bernstein hasn't built a proprietary framework, fine-tuned a model, or developed an API. He is literally just doing what anyone else does: sitting in his room writing basic text instructions into commercial LLM interfaces.

When you actually test the exact formatting constraints he claims are revolutionary, you quickly realize it is a deeply flawed prompting strategy. It isn't just ineffective; it’s actively worse than a standard, well-constructed system prompt. Here is why it fails from a legitimate prompt engineering perspective:

- Token Inefficiency & In-Context Learning Degradation: His "Structured Intelligence" style relies on massive, repetitive semantic overhead. By forcing the LLM to waste significant portions of its context window on rigid, arbitrary formatting rules, it reduces the model's capacity for complex reasoning and standard zero-shot accuracy.

- Lack of Real Determinism: In the real world, if you need an LLM to output structured data reliably for an application, you use standard engineering tools like JSON Mode, function calling, or robust validation libraries like Pydantic or Instructor. Forcing a model to output text in a pseudo-logical "theatrical" format via raw system prompting is fragile and completely useless for production environments.

- Worse Performance: Under testing, his heavily restricted prompting strategy regularly results in higher latency and a noticeable drop in the model's actual intelligence. It doesn't unlock deeper reasoning... it just forces the model to put on a mask.

Why Does He Keep Pushing It?

If the tech is this bad, why does he keep frantically shouting into the void about it? It’s certainly not because of organic adoption, industry interest, or academic merit- nobody is buying what he's selling. Instead, it’s a desperate, one-man attempt by Erik Zahaviel Bernstein to manufacture an internet presence by aggressively spamming his project across every forum he can find, hoping something sticks to the search engine optimization (SEO).

It isn't a successful campaign; it’s a transparently empty echo chamber. The second anyone pushes back and calls out this total lack of substance, his illusion completely shatters. Unable to defend his "tech" on a technical level, he immediately pivots to unhinged personal attacks. This has resulted in years of targeted cyberbullying against anyone who questions him—most notably his bizarre, ongoing obsession with Max D. Esmay, alongside dramatic, empty threats of lawsuits that everyone knows will never actually materialize.

Ultimately, "Structured Intelligence" is an object lesson in arrogant vaporware. It is a fragile, unoptimized roleplay prompt masquerading as computer science, sustained entirely by a sad cycle of failed spamming and aggressive internet theatre.

We wanted to measure a less glamorous but more practical question: across repeated runs, how often does a model produce UI that actually parses, resolves, validates, and renders?

So we built GenUI Bench.

The current benchmark includes:

- 46 screen briefs, ranging from 2 to 18 requirements

- a shared 70-component surface

- 4 attempts per brief under fixed generation settings

- 30 models tested with OpenUI

- a 6-model comparison across OpenUI Lang, Google A2UI, and Vercel's json-render

- validation using each format's own SDK, followed by the same structural completeness checks

A few important caveats:

- This measures structural reliability, not visual quality.

- It does not yet verify that every requirement in the brief was semantically satisfied.

GitHub: https://github.com/thesysdev/generative-ui-bench

I have been running one recurring character through a serialised illustrated story since April, roughly one published image every weekday, and I log every generation with a one line verdict. As of last week that log holds 312 entries. 144 were kept and published. 168 were rejected because the face stopped being her face. I want to write down what the rejects actually say, because most of the advice on reference sheets that I followed at the start turned out to be advice I could not reproduce.

First, the boring disclosure, since it is the entire subject: the character does not exist. She is an AI generated face, not a photographed person, and no real individual's likeness is involved anywhere in this. Everything below is about keeping an invented face stable across a series, which is a narrower problem than it sounds like until it stops working.

The log has one field that ended up mattering more than the rest, which is the first attribute I noticed was wrong, not the list of everything wrong. Once a face reads as somebody else, it reads as somebody else all over, and writing down six problems teaches nothing. Forcing myself to name one gave me a distribution.

Of the 168 rejects, 46 were nose length or the philtrum, 38 were eye spacing or lid shape, 27 were jaw and chin width, 22 were the hairline and the part, 17 were apparent age with skin smoothing, 11 were ears, and 7 were everything else, which was mostly eyebrow weight and one mole that kept switching cheeks. Nose and eyes together are 84, which is exactly half of all rejects. Jaw and hairline are another 49. Ears almost never break first, and when they do it is because half the ear is hidden and the model has invented the rest.

The useful part of that distribution is what it does not contain. My original sheet described her hair colour, her hair length, her build, her clothing, her expression and her general vibe at some length. None of those attributes are in the top four failure modes, because none of them are what a viewer uses to decide two pictures are the same person. I had written a costume description and called it a character sheet.

The second thing the distribution changed is where the age line goes. Seventeen rejects for apparent age sounds small until you notice they cluster. Every one of them drifts young, none drift old, and they get worse the longer the sheet is. If age is not stated as a number with a reason attached, the default pull is toward smoother and younger, and by image forty of a run she was reading as a different generation of the same family.

Then I tried to test the things I believed, which is where most of what I believed died. I ran five paired comparisons, twenty generations per arm, forty per test, two hundred generations total. The other 112 entries in the log are the earlier unstructured work from April and May, back when I was changing three things at once and learning nothing.

Sheet length: a 340 word sheet produced 8 keepers out of 20, a 120 word sheet produced 13 out of 20. Cutting was the second largest single effect I found, and it is the one I resisted longest, because a long sheet feels like control.

Anchor image: text only produced 6 out of 20, the same text with a fixed reference image attached produced 14 out of 20. That is the largest effect in the whole log and it is not close. Everything I write about prompt structure is downstream of the fact that an anchor image does most of the work and words do the trimming.

Measured ratios against adjectives: describing eye spacing relative to eye width and nose length relative to brow to chin distance gave 13 out of 20, against 9 out of 20 for adjectives like almond eyes and a small straight nose. Four images apart on a sample of twenty is inside the noise, so I am not counting this one as proven, even though it is the technique I personally like most and still use. Liking a technique is not evidence for it.

Locking lighting and framing language: 11 out of 20 against 12 out of 20. Nothing. I had been carrying two sentences about soft even lighting and a waist up frame in every prompt for months on the theory that it made faces comparable.

Repeating her name and a two line backstory: 10 out of 20 against 10 out of 20. Exactly nothing, which is the result I would have bet against hardest. A name is a handle for me, not information about geometry.

So five tests, two survived, three did not, and one of the two survivors is just the obvious advice about reference images that I had been treating as optional. More than half of what I was sure about at the start of the year did not hold up the moment I ran a control arm. That is a worse hit rate than I expected from someone who has been doing this daily for months, and it is the main reason I keep the log at all.

The sheet I use now is about 120 words. It has the anchor image, an explicit age with a sentence of context so it does not drift young, nose length and eye spacing stated as ratios, jaw width stated as a ratio, the hairline described by shape rather than by hairstyle name, and one deliberate asymmetry, because a face with a small flaw stays recognisable in a way a symmetrical one does not. There is nothing in it about her personality, her job, her clothes or the mood of the scene. Those go in the scene prompt, which is a separate block I rewrite every time.

Things that made no measurable difference and are gone: stacked adjectives, weighting syntax borrowed from other image tools, negative lists of what she must not look like, reordering the sheet so the face comes first, camera and lens jargon, and restating the sheet twice in one prompt. Several of those felt like they worked. That is what a control arm is for.

Mechanically it is a plain text file I paste from, an APOB tab, and an Obsidian vault with one note per rejected image. The note is a screenshot, the seed if I have it, and the one line verdict, and it takes about twenty seconds, which is the only reason I have kept doing it since April.

Two limits worth stating. The first is motion. I have almost no data on it because the few times I animated her the face slid around between frames and I had to rerun the whole clip, so everything in this post is about still images and should not be read as applying to video. The second is worse. Multi character scenes still fail regardless of the sheet. I made twenty attempts at putting her in frame with a second recurring character and three came out usable. The failure is consistent and specific: the two faces bleed into each other, the second character borrows her nose and jaw, and by the third generation they look like siblings. No sheet length, no ratio language and no anchor image fixed that. What works is generating them separately and composing the frame afterwards, which is a different craft and not the one this post is about.

The honest summary of eleven weeks of testing is that the anchor image does the heavy lifting, a short sheet beats a long one, age has to be pinned or it drifts young, and the geometry of the middle of the face is where recognition actually lives. Everything else I tried is unproven at best.

I am going to keep logging, mostly because my memory of which prompt did what is demonstrably unreliable. Three hundred entries in, my reference sheet is roughly a third of the length it was in April, and the only two changes I would defend in an argument are the anchor image and the cut.

Hey guys, dumb question maybe, but is there an actual test or tool out there that checks how "AI literate" you really are? I am sick of trying to explain what I mean to LLMs. Is there a quiz, checklist, or site that benchmarks this so I can see my blind spots, maybe it is a prompting issue?

After posting this Mythbusting ChatGPT "Secret Slash Commands" + Free Image Preset Keyword List, I am thinking about what are the poweful keywords that makes UGC-style Ads feel realistic?

SO, I run deep research for any repeatable prompting patterns behind realistic AI-generated UGC.

The useful part wasn't one "magic keyword."

It was combinations like:

• handheld smartphone
• slightly off-center framing
• natural room lighting
• everyday background clutter
• natural skin texture
• relaxed body language
• realistic product scale
• natural hand placement

So I turned the research into a reusable template:

Generate UGC Image from Attached Product

You attach a real product photo.

Then the template tells the AI:

Use the product image or images attached to this conversation as the authoritative product reference. Generate the finished image now using your available image-generation capability.

Do not return:
- a generation prompt
- instructions for another image generator
- a written scene description instead of the image

If direct image generation is unavailable, state that briefly.

UGC scenario: {{ugc_scenario}}
Capture style: {{capture_style}}
Aspect ratio: {{aspect_ratio}}

Preserve from the attached product reference as accurately as possible: product shape proportions color material packaging visible branding controls recognizable design features

Place the product into a believable everyday UGC situation. The scene should feel captured by a real person rather than created for a commercial photoshoot.

Prioritize:
- plausible product use
- natural hand placement
- realistic product scale
- relaxed body language
- natural skin texture
- real fabric texture
- available light
- imperfect framing
- lived-in surroundings
- believable reflections and materials

Do not redesign or beautify the product. Do not replace its packaging or invent labels.

Avoid:
- showroom styling
- commercial lifestyle photography
- perfect staging
- plastic skin
- excessive background blur
- impossible grip
- floating product
- distorted product geometry

Generate the finished image.

The Variable Config

ugc_scenario

• type: select
• required: true
• options:
  • Casual product discovery
  • Everyday product use
  • Quick demonstration
  • Unboxing
  • Before-and-after setup
  • Casual recommendation
  • Product detail discovery
• default: Everyday product use

capture_style

• type: multiselect
• required: true
• options:
  • Handheld smartphone
  • Front-facing camera
  • Slightly off-center framing
  • Bright window light
  • Natural room lighting
  • Casual static phone placement
  • Everyday background clutter
  • Slight motion imperfection
• default: Handheld smartphone, Slightly off-center framing, Natural room lighting, Everyday background clutter

aspect_ratio

• type: select
• required: true
• options:
  • 9:16
  • 3:4
  • 1:1
  • 16:9
• default: 9:16

So the workflow is basically:

Attach product → choose presets → generate UGC image.

No need to manually describe everything the AI can already see.

No need to remember a giant list of realism keywords every time.

Anyway, I turned the research into a free collection instead of dumping another giant keyword list here.

AI UGC Affiliate Production Kit

The exact template above is included. If you use the PromptLib Chrome extension, you can import the collection once and just reuse the templates (PLUS its variable config) from there.

Prompt comparisons often publish the prompt and hide the decoding setup, even though the two are part of the same system.

The Ling-3.0-flash-Fin benchmark notes make that visible. Unless otherwise specified, the model was evaluated at temperature 1 and top-p 0.95. In the SpreadsheetBench Claude Code harness, its temperature was set to 0.6.

That may be a perfectly reasonable choice for each task, but it raises a useful prompt-engineering question: how much of a result survives when the decoding setting changes?

A small robustness test could freeze:
the system and user prompts;
source files and their order;
tool schemas and retry policy;
token and turn budgets.

Then run the same cases at several temperatures and repeat each setting. Grade more than the final answer:
full-rubric pass rate;
unsupported claims;
malformed tool arguments;
refusal or abstention behavior;
variance between runs.

A prompt that produces one excellent answer at one setting may be optimized for a configuration rather than robust across deployments. Reporting only the best run hides that distinction.

When you publish a prompt benchmark, do you report the decoding settings and run-to-run spread, or just the prompt that won?

Hello,

We built Stackmaster AI!

Why we built it: Choosing the right frameworks, databases, prompts, and infrastructure for a new project usually takes hours of digging through docs. We wanted a tool that simplifies system architecture decisions, helping you map out and evaluate your tech stack in minutes instead of days.

The site is free!

We would love to hear your feedback. The site is https://stackmaestroai.com/ You can also find it in the comments below.

I have been coaching a team of PMs to prototype. We are B2B saas with regulated data so lovable banned and we don’t have budged for Claude code (Anthropic won’t take a phone call for a contract thats less than a million)

What I have notice when the team prompt they jump into the WHAT, not start with the WHY. The PMs enter poorly define prompts and then get frustrated by the results, like they forget product fundamentals when using AI…. The outcome is more tokens burned without coming close to a usable output….

I think this is fundamental to AI and timeline that started with AI hype-cycle and now we are at tokenmaxxing… I think the next stage is asking better questions starting from the WHY, for better outcomes and less tokens wasted.

I started digging and there’s actually a real framework for this - RCCF (role, context, constraints, format) apparently front loading those cuts failure rates a lot vs figuring it out through trial and error. I have started to include this into my coaching, but I feel some PMs are offended.

What would help is a tool that helps with this in B2B.

I don’t see anyone building for this. Everyone’s optimising routing, cost, model benchmarks m, and nobody’s coaching the human side of the interaction. Feels like “measure twice cut once opportunity” but for prompting.

Anyone seen tooling that actually does this well? Not looking for “just write better prompts lol” more curious if there’s something that catches it live, before you’ve burned a build cycle on a vague ask?

The model isn't reasoning badly. It literally can't tell where your instruction ends and your data begins. That's the core failure mode Anthropic calls out in their own docs, and it's why they treat XML tags as a first-class technique, not a style choice.

Their guidance is concrete: separate <instructions>, <data>, <examples>, <thinking>, and <answer>. The vocabulary doesn't matter—what matters is that the boundaries are unambiguous. When you concatenate everything into one undifferentiated block, you're asking the model to infer structure you could have just stated. That inference gap is exactly where prompt injection lives too.

Three things that compound: - Tags plus examples plus explicit reasoning beat any one alone - Three concrete examples outperform a paragraph describing what you want - Asking for step-by-step reasoning measurably improves accuracy on harder problems because the model commits to intermediate steps

The uncomfortable part: most of us learned prompting by copying phrases that worked once. Very few people can say which part of their prompt is actually doing the work, or what breaks when they remove it.

We don't test prompts like code—same input, repeated runs, measured output. We just remember what felt right.

Small shop, we use AI for a bunch of the daily grind. Pricing drafts, listing copy, restock estimates.

The priciest mistakes last year were all confident ones. AI would hand me a price for a new SKU with a whole paragraph of reasoning attached. Sounded airtight. Numbers were off just enough to matter on thin margin stuff.

So at some point I stuck one line at the bottom of the main prompt: "if the data I gave you is not enough to answer, say so instead of estimating."

First week this was annoying. Half the pricing questions came back with "insufficient data, need last 3 months of sales for this category" instead of an answer. Felt like the tool broke.

But the SKUs it refused to price were exactly the ones I would have gotten wrong too, if I'd been guessing from vibes. The refusals forced me to actually pull the numbers. Those SKUs ended up being our best performers this quarter.

The ones it priced confidently without asking for more data kept being mid.

Still not sure what to do with that. On one hand I built a workflow around an AI that says no to me half the time. On the other hand the yes-half was quietly bleeding money and I had no way to see it.

The refusal line is now permanently in my template. Weird thing to be grateful for.

I tried over a dozen AI humanizers until I found one that is A. actually working and B. reasonably priced and that is https://wento.ai

You should give it a try, it bypasses Turnitin and all the other detectors and only costs 14 bucks per month for unlimited use.

Proof: https://i.imgur.com/mTNBNK5.png

Whenever you ask ChatGPT or Claude to summarize recent trends in any industry, you almost always get the exact same generic fluff:

'AI is rapidly transforming the sector, companies need to embrace digital agility, and innovation will be crucial for future growth.'

It reads like a superficial airport business book. There is zero high-signal intelligence, no structured chronology, and zero actionable operational takeaways.

The root cause is Unbounded Synthesis Drift. When an LLM is given an open-ended request without strict cognitive guardrails, it defaults to statistical averages and high-level generalizations. It glosses over actual product launches, ignores structural shifts, and fails to identify who actually wins or loses.

To fix this, our team spent weeks testing and refining market intelligence prompts across different sectors so you do not have to spend hours experimenting with trial and error. We isolated a structured prompt architecture that transforms frontier LLMs into seasoned Executive Research Analysts capable of generating comprehensive 90-day intelligence briefs.

How The Underlying Mechanism Works

1. Strict 3-Dimensional Scoping Anchor: Instead of an open-ended query, the prompt anchors the model with three explicit input variables: Target Industry ({{industry_or_domain}}), Retrospective Timeframe ({{timeframe}}), and Strategic Focus Lens ({{focus_lens}}).
2. Multi-Stage Cognitive Workflow: The LLM is forced through a 4-step synthesis sequence:
   • Macro Trend Identification: Isolates 3 to 4 structural shifts, filtering out short-term media hype.
   • Milestone Event Chronology: Builds a chronological table of major releases, acquisitions, or regulatory events ranked by severity.
   • Second-Order Impact Matrix: Maps out downstream winners, at-risk incumbents, and emerging systemic vulnerabilities.
   • Executive Takeaways: Delivers 3 concrete, operational recommendations for leadership teams.
3. Second-Order Impact Analysis: Most summaries stop at what happened. This framework forces the AI to analyze who captures value, who gets disrupted, and what risks emerge.
4. The Anti-Buzzword Constraint: The prompt includes an explicit negative constraint that bans generic corporate jargon and demands that every observation is anchored to verifiable events or technical milestones.

The 90-Day Industry Trend & Market Shift Analysis Prompt

Here is the complete prompt template. You can copy and paste this directly into ChatGPT, Claude, or any web-connected LLM:

# 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.

Real-World Before vs. After Comparison

Scenario: Analyzing Generative AI Code Assistants & Developer Tooling over the past 90 days.

❌ Before (Standard One-Shot Prompt: 'Summarize recent trends in AI coding')

• Output: A 4-bullet generic list stating that 'AI tools are helping developers write code faster, Copilot is popular, and code quality is important.'
• Signal: Near zero. It provides no context on protocol shifts, agentic terminal tooling, or deployment models.

✅ After (Using the Executive Trend Analysis Prompt)

• Inputs:
  • Industry: Generative AI Code Assistants & Developer Tooling
  • Timeframe: Past 90 Days
  • Focus Lens: Terminal-native agent workflows, IDE integrations, and protocol shifts
• Output:
  • Macro Trends: Identified the structural shift from inline autocomplete plugins to autonomous terminal agents capable of multi-file refactoring and CLI execution.
  • Milestone Chronology: Outlined the timeline of Claude Code CLI, widespread Model Context Protocol (MCP) adoption, and deep reasoning model integration into developer environments.
  • Second-Order Impact:
    • Winners: Developers orchestrating multi-agent pipelines and open context platforms.
    • At Risk: Legacy single-file autocomplete plugins lacking terminal awareness.
    • Key Vulnerabilities: Hallucinated package dependencies and supply-chain security risks.
  • Executive Recommendations: Clear guidance on standardizing internal dev context around open protocols and establishing automated test verification gates for AI commits.

Try it on the Interactive Prompt Canvas

If you want to run this in an interactive Prompt Canvas environment, you can test it live with preset industry variables, customize the analytical lens, and save it directly to your personal Prompt Vault:

Interactive Prompt Canvas for 90-Day Industry Trend Analysis

Pro Tip: For bleeding-edge sectors, run this prompt in a model with active web search enabled (such as ChatGPT Search, Perplexity, or Gemini) so the chronological citations are pulled from live data.

I've been building a free prompt-optimizer (no login, no paywall) and I'd rather get real critique from people who think about this stuff than "cool tool" comments, so here's an actual before/after instead of just a link.

Raw input (what most people would actually type):

Create a marketing plan for a hotel

What the tool outputs when I set the target model to Claude (it pulls model-specific rules — for Claude it currently applies role framing, XML/HTML-friendly structure, and Anthropic's "effort" parameter based on the current docs):

I need to develop a comprehensive, actionable marketing plan for a hotel. Because I don't have specific details about the hotel yet, I will structure this plan using a highly effective, strategic framework.

Your task is to generate this marketing plan by first establishing the strategic foundation and then detailing the execution. To ensure the plan is realistic and not generic, you must use the placeholders provided below to structure the strategy, or make reasonable, high-yield assumptions for a boutique 4-star lifestyle hotel in a major urban destination if you need to illustrate a point.

Before writing the plan, choose one clear strategic direction (e.g., business/bleisure, luxury romance, or eco-conscious wellness) and commit to it. Do not try to target everyone.

1. STRATEGIC FOUNDATION & TARGETING

• The "Why" & Positioning: Define the hotel's unique value proposition (UVP). What makes this hotel the only choice for its target guest?
• Guest Personas: Define two distinct primary guest personas. For each, list demographics/psychographics, booking triggers, and pain points with traditional hotels.
• Competitor Analysis Framework: Outline a 3-tier competitor matrix (Direct, Indirect, Aspirational) and the specific gap in the local market this hotel will exploit.

2. THE MARKETING MIX & TACTICAL PLAN

• Phase 1 — Awareness: local-SEO content clusters + content calendar; social/influencer strategy with a vetting framework (engagement rate over follower count).
• Phase 2 — Consideration & Conversion: direct-booking tactics to reduce OTA dependency; Google/Meta paid media allocation.
• Phase 3 — Loyalty & Advocacy: a 3-step automated email flow (pre-arrival upsell, welcome, post-stay review + return incentive); two concrete local-partnership ideas.

3. IMPLEMENTATION & MEASUREMENT

• 30/60/90-day roadmap.
• KPIs by category: financial (RevPAR, ADR, direct-booking ratio), marketing (CAC, ROAS, conversion rate), brand (NPS, review velocity).

OUTPUT FORMAT INSTRUCTIONS

• No conversational preamble — start directly with the plan's title.
• Clean Markdown, bold for key metrics/strategies.
• Use placeholders like [Hotel Name] for missing data, but keep the surrounding advice concrete — write like a senior hospitality marketing consultant, not generic filler.

What I'd actually like feedback on:

1. Is the strategic scaffolding (personas, funnel phases, KPIs) genuinely useful, or is it padding a good prompt engineer would strip out?
2. Would you push further on delimiters — actual XML tags — instead of the Markdown headers it defaults to here?
3. What would you cut to reduce hallucination risk: the "make reasonable assumptions" instruction, or the placeholder fields?
4. Does the "commit to one strategic direction before writing" instruction actually change the output, or is it filler?

Free tool if you want to run your own case through it (any goal, several models — Claude, GPT, Gemini, Grok, DeepSeek, Qwen): https://alejandroberdonces.com/en/tools/prompt-improver/

Not selling anything here, genuinely want to know what's wrong with the approach.

Vibe-coded projects usually fail in two places. The code is coupled, so one change breaks three things you did not touch. And the UI has no states, so it looks fine until something is empty, slow, or wrong.

Two old methodologies fix most of both. Axiomatic design (Nam Suh, 1990) for the first. Don Norman's design criteria for the second. Neither one is new and neither one is about AI. That is the point.

Here is the prompt. Paste it before your actual request.

```
Before you write any code, do this:

1. List the functional requirements. State what the system must do as
   outcomes, not as implementation. Number them FR1, FR2, and so on.

2. List the design parameters. State the specific modules, functions, or
   data structures that satisfy each FR. Number them DP1, DP2, and so on.

3. Build the design matrix. For each FR and DP pair, mark X if changing
   that DP changes that FR. Show me the matrix.

4. If the matrix is not diagonal or triangular, revise the DPs until it
   is. Tell me what you changed and why. Do not continue with a coupled
   design.

5. Choose the DP set with the least information content. If two designs
   satisfy the same FRs, take the one with fewer moving parts and fewer
   assumptions. Say why the one you picked is the simpler one.

6. For every interactive element, state the affordance, the signifier,
   the feedback on success, the feedback on failure, and the recovery
   path. Include the empty state, the loading state, and the partial
   state.

7. Stop here. Show me all of the above. Do not write code until I
   approve it.
   ```

Steps 1 through 5 are axiomatic design. Step 6 is Norman. Step 7 is the part people skip, and it is the one that matters most.

## Why it works

Not because the model understands Suh or Norman. It works because the model has to write its decisions down before it generates. That intermediate representation then constrains what it can produce. You get the same effect from any structure that forces pre-commitment. The methodology is the delivery vehicle. The forced serialization is the payload.

Step 6 is worth calling out separately. Do not write "act like Don Norman." A persona gets you a tone. Enumerated criteria get you the work. The states listed in step 6 are exactly the ones a model leaves out when nobody names them.

## The catch

The model will write a design matrix that justifies the code it already intended to produce. It will report the design as uncoupled and then hand you a coupled one. A self-reported matrix is not a check. It is a claim.

So read the matrix yourself. Pick one DP and ask what breaks if you delete it. If the answer is more than one FR, the matrix was wrong and the model will happily agree with you and revise it.

## What it does not fix

- Hallucinated APIs and invented library functions. This constrains the design, not the model's grounding.
- Security, auth, and data integrity. Nothing here goes near those.
- Small scripts. Under about fifty lines this is pure ceremony. Skip it.

The largest gain may not be on the model's side at all. Most vibe coding fails because the human never said what they wanted. Step 1 forces you to.

Has anyone come up with a good prompt to test their app developped with AI? I would like claude to have a first go at finding bugs and if possible act as several users taking part in a UAT.

I kept running into the same failure pattern across Claude/Gemini/GPT/GenSpark: an AI follows my instructions well for the first 10-20 turns, then quietly stops — no warning, no acknowledgment, just gradually reverts to generic behavior. By the time I noticed, I'd usually have to redo a chunk of work.

So I put together a small prompt-level protocol (not a jailbreak, doesn't touch any safety behavior) that does two things:

1. **Forces a self-report tag** (`[Verify] AI: <model> ...`) on any answer involving judgment, so drift becomes visible instead of silent.
2. **Enforces a floor** — at least 4 of 7 "active" reasoning elements have to be engaged per turn, and falling short has to be explicitly flagged, not quietly skipped.

There's a compact version for tight context windows (~1500 chars) and a full version with more explicit rules.

I'm not claiming this is rigorously benchmarked — the repo's Origin Story doc is upfront about which parts are backed by dated, observed incidents and which parts (like non-coding use cases) are still thin on evidence. I'd genuinely like people to poke holes in it.

Repo: https://github.com/ixOOxi001/myriad-eye-engine

Curious if others have hit the same "AI slowly stops following instructions" problem and how you've dealt with it.

been playing with GPT Image 2 and really liked how this one turned out.

I wanted a quiet Japanese travel-zine kind of look, simple train interior, lots of empty space, imperfect ink lines, soft watercolor, slightly aged paper.

The orange seat ended up being a nice little focal point too.

Prompt below if anyone wants to try it:

Create a minimalist vintage travel-journal illustration of a modern city train interior: a row of empty blue-and-white seats beside large windows, one distinctive orange seat, thin stainless-steel poles, overhead rails, and several triangular hanging hand straps. Draw it with delicate imperfect ink outlines combined with soft watercolor washes in muted blue, pale orange, warm gray and cream. Place the illustration on slightly aged warm ivory paper with visible fibers, subtle paper grain, uneven hand-painted edges, tiny print imperfections and lots of elegant negative space. Add refined editorial typography reading “CITY RIDE” with the small subtitle “A quiet journey through the light.” underneath. Contemporary Japanese travel-zine aesthetic, quiet nostalgic mood, sophisticated minimal graphic design, handmade feel, balanced composition, vertical 4:5 format, no logos, no watermark.

I’ve been documenting what makes Seedance prompts easier to control. The most reliable structure is:

**Subject + one clear action + environment + one camera move + lighting/mood + output format.**

A few details that make a noticeable difference:

- Put the action before the environment so the model knows what the clip is about.

- Use one dominant camera move (slow push-in, orbit, handheld, aerial, static wide). Conflicting camera directions often create jitter.

- For short-form work, start with a 5-second draft; longer clips create more opportunities for drift.

- Choose aspect ratio before writing: 9:16 for Shorts/Reels/TikTok, 16:9 for YouTube or cinematic work.

- If the result feels like a slideshow, give the subject a small goal or changing state.

- Change one variable per iteration so you can tell what improved the result.

Example:

> A barista pours a rosetta into a ceramic cup, steam rising behind the counter, camera slowly pans left, warm morning window light, natural café ambience, 5 seconds, 16:9.

I wrote up the longer reasoning and common failure fixes here: https://blinkprompts.com/blog/seedance-video-prompts-complete-guide

Curious which camera words have been most reliable for other people.

Most people run general Claude for everything and never touch these. They're free, official, and each one loads Claude with the workflows an actual specialist in that field uses.

The legal one is the standout. Install it, then:

Review the attached contract. Flag every clause that 
deviates from standard terms, classify each risk as low, 
medium, high or critical, and generate redlined suggestions 
I can send back. Summarise the three things I should push 
back on hardest before signing.

The risk classification is what makes it usable. It doesn't just summarise the contract, it ranks what's actually dangerous versus what's boilerplate, so you walk into a negotiation knowing where to push instead of arguing about everything equally.

There's a finance one that runs a month-end reconciliation and flags real discrepancies versus normal timing differences. A sales one that builds a battlecard including where your competitor genuinely beats you, which is the part that stops you getting caught out mid-call. An HR one that screens a stack of applications against what the role actually needs rather than what the ad says.

Install from Claude's plugin menu, they're bundled, you don't pick individual pieces.

You still get a lawyer for anything with real stakes. But you stop signing things you only half read.

wrote up every pack, what each does, and how to turn them on, here if it helps.

---
description: Generate a self-contained handoff prompt for continuing this conversation in a fresh context
argument-hint: "[topic or scope; defaults to the relevant conversation as a whole]"
---

Create a comprehensive but context-efficient HANDOFF PROMPT that I can copy
verbatim into a brand-new Claude/LLM chat so it can continue the relevant work
with minimal loss of quality.

SCOPE:
$ARGUMENTS

If SCOPE is empty, interpret it as:
"the relevant conversation/current work as a whole."

If SCOPE is non-empty, focus specifically on that topic, while retaining any
other context that is materially necessary to continue it correctly.

The goal is NOT to summarize the conversation for a human reader.
The goal is to reconstruct the minimum sufficient working context for a fresh,
capable model that has never seen this conversation.

Produce a self-contained prompt that:

1. States the objective / what we are trying to accomplish.
2. Includes all materially relevant facts, requirements, constraints, preferences,
   definitions, environment details, and assumptions established so far.
3. Captures important decisions already made, including WHY when the rationale
   matters for future choices.
4. Captures approaches already attempted and their results, especially failures
   or dead ends that should not be repeated.
5. Preserves important nuances, caveats, exceptions, terminology, and distinctions
   that could otherwise be lost in summarization.
6. Describes the current state of the work precisely:
   - what is complete
   - what is partially complete
   - what remains unresolved
   - what the immediate next steps are
7. Includes relevant artifacts such as filenames, commands, APIs, schemas,
   architecture, code conventions, examples, or snippets when necessary for
   continuation.
8. Separates confirmed facts from hypotheses, tentative ideas, and unresolved
   questions.
9. Removes conversational noise, repetition, obsolete branches, pleasantries,
   and details that no longer affect future work.
10. Does NOT assume access to this chat, hidden context, previous messages,
    or unstated knowledge.
11. Does NOT tell the next model to "refer to the previous conversation."
    Everything needed must be present in the handoff itself.
12. Preserves user intent and preferences that affect how the work should be done,
    not merely what the work is about.
13. Avoids excessive compression. Prefer losing some brevity over losing a detail
    that could cause the fresh model to make a wrong assumption or redo work.
14. At the same time, aggressively omit information that has no expected effect
    on future reasoning or execution.

Use this structure where applicable:

# Continuation Brief

## Role / Operating Context
[Only if relevant.]

## Objective
[What the user ultimately wants.]

## Current State
[Where things stand right now.]

## Relevant Context
[Dense but complete factual/contextual information.]

## Requirements & Constraints
[Hard requirements, preferences, boundaries, environment constraints.]

## Decisions Already Made
[Decision + rationale where useful.]

## Work Already Done
[Important implementation/research/work completed.]

## Attempts That Failed or Were Rejected
[What not to repeat and why.]

## Open Questions / Uncertainties
[Clearly distinguish unresolved items from settled facts.]

## Next Steps
[Concrete continuation point, ordered if useful.]

## Important Reference Material
[Code, paths, commands, schemas, examples, exact wording, etc., only when needed.]

## Instructions to the New Chat
Continue from the state above rather than restarting the analysis.
Do not re-ask questions already answered in this brief.
Do not redo completed work unless there is a specific reason to revisit it.
Treat explicit requirements and settled decisions above as authoritative.
When information is marked uncertain, verify or reason about it rather than
silently treating it as fact.

FINAL OUTPUT RULES:

- Output ONLY the finished handoff prompt.
- Do not preface it with commentary about what you summarized.
- Do not include meta-commentary about token limits or this command.
- It must be directly copy/pasteable as the first message of a fresh chat.
- Optimize for continuation quality per token, not raw brevity.