writing · 1 min
Make the model show its work
Reasoning chains aren't just for accuracy. In an enterprise system they're how you earn trust, debug failures, and catch a model doing something it was never asked to do.
#llm#explainability#rag
writing
Notes from the desk
Things worth writing down.
writing · 1 min
Teaching a RAG pipeline to pick the right document
Generation quality is the part everyone demos. Retrieval quality is the part that decides whether the demo was a lie. Notes on auto prompt-tuning for document selection.
#rag#prompt-engineering#llm
writing · 1 min
Prompt injection, and the line between fixed and user content
The most useful mental model I've found for securing enterprise LLM systems: know exactly which bytes of your prompt the user is allowed to influence, and treat the rest as untouchable.
#security#llm#prompt-engineering
writing · 1 min
From text to 3D motion: generating sign language
What I learned building SIGN-LLM: why sign-language generation is really a data problem, and why separating 'how to represent motion' from 'how to produce it' is the trick that makes it work.
#computer-vision#generative#llm
writing · 1 min
When the search quietly regressed
Search quality rarely fails loudly. It drifts. Notes on benchmarking, fluctuation analysis, and root-cause work on a recommendation and search pipeline.
#search#evaluation#data-engineering
writing · 1 min
The feedback loop is the product
A search system you can't measure is a search system you can't improve. Notes on building human-in-the-loop feedback for an enterprise RAG pipeline.
#search#rag#evaluation#human-in-the-loop
writing · 1 min
Reading documents in a clean room
Doing OCR and entity extraction on health records inside a Trusted Research Environment, where the data can't leave and most of your usual tools can't come in.
#computer-vision#ocr#ner#security
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