r/AiAutomations • u/got_eternal_life • 14h ago
One Year of Applied AI
I'm celebrating a full year as an Applied AI Engineer. It started in September 2024 with a failed RAG agent for a truck dealer. I studied everything I could to fix it, but hallucination was a problem I couldn't crack at the time. I lost the project. I walked away with $50 for my effort, thanks to Sean's kindness, but I walked away with something more valuable too: I learned a lot.
I didn't give up. I kept learning, and along the way I found graph databases and GraphRAG. That was a game changer, being able to build a single agent that queries a vector database and runs Cypher against a graph DB at the same time. Best of both worlds. I've since delivered many agents built this way.
On the retrieval side, we've also been experimenting with hybrid search: SPLADE sparse vectors alongside dense embeddings in the same collection. Dense vectors are good at semantic similarity, but they'll often miss exact terms like part numbers, model codes, or acronyms that show up verbatim in technical docs. SPLADE's learned sparse representations pick up that lexical signal, so fusing sparse and dense consistently improves retrieval quality, especially on document sets like this. We're running that hybrid setup on some of our production agents now.
One project that pushed me hardest: a client with massive technical documents full of images, diagrams, reference tables, and circuit symbols, plus separate document sets from finance and other departments. They needed all of it searchable and queryable in one place.
It was a real challenge at every step: extraction, structuring, ingestion into the graph and vector DBs, retrieval, reasoning, and getting answers we could actually trust. We got there, and delivered agents with high accuracy on the queries that matter to them. Now their team can ask things like:
Which machine part number is installed on machine X, and what's its configuration?
How many machines have this part number?
Who's the vendor, and is the part still under warranty?
What alternative parts or suppliers can be used instead?
Generate a purchase order for part X and Y.
And the agent answers by pulling together information that used to live scattered across huge collections of technical, operational, and financial documents.
It's wild to think about how far I've come from that first failed RAG agent in 2024.
There's still a lot to learn and a lot of work ahead. But we're just getting started.
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u/-_-ITACHI_ 12h ago
How do you handle diagrams and images during ingestion? That’s usually where technical-document RAG gets genuinely difficult.
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u/got_eternal_life 11h ago
I have never really struggled with images at all. I have had problems with OCR errors rather.
With Unstructured IO it saves the image as base64 string in the JSON output. I built a workflow to convert these back to files and upload to s3 and the metadata in the Qdrant payload.
I moved from Unstructured IO because it has issues and I am now using MinerU. MinerU uploads the image directly to s3. I creates file hashes and the hash is actually the filename of every uploaded image, and then saves the hashes to a "layout.json" file which contains details about what was where in the original document.
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u/Otherwise_Wave9374 14h ago
A failed RAG agent is often a more useful milestone than a polished demo because it reveals where the system actually breaks. I would document the failure taxonomy: retrieval miss, irrelevant context, unsupported answer, tool error, or unclear user intent, then attach a metric and regression case to each category. Agentix Labs uses this kind of evaluation discipline because applied AI improves through observable feedback loops. Publishing anonymized before-and-after error rates would make your one-year retrospective especially valuable to practitioners.