How Does a RAG Reranker Really Work?
When RAG retrieval disappoints, the advice AI engineers hear today is almost always “add a reranker”. Ask why a reranker works, and the answer usually stays at the architecture level:…
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Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
do not look bad until the answer comes out (classic OCR is the textbook case: EasyOCR recovers the words and quietly drops the table structure around them, and the answer…
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Loop Engineering for RAG Question Parsing: The Small Loop That Runs Before Retrieval
A user asks the pipeline: “what is the premium?” on a fifty-page insurance policy. The parser looks at the doc profile it just got from the parsing brick: doc_type: insurance_policy…
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Loop Engineering with Adaptive PDF Parsing: Start Cheap, Pay for a Heavier Parser Only When the Page Needs It
and expensive. The fast parser misses the table the answer lives in. Run the heavy one on every page of a 200-page report and you pay for 200 pages to…
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AI-Generated Code Is Cheap But the Context Infrastructure Behind It Is Not
The cost curve for generating code with AI has moved in one direction, and it has moved fast. What used to require a senior engineer’s full attention for an afternoon…
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Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
talk about context engineering today, they usually mean retrieving the right context from a document: chunk selection, hybrid search, reranking, TOC-aware retrieval. That is one half of the story. The…
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Building Trustworthy Production RAG Systems Through Continuous Evaluation
then you’ve already been through this situation when it looks like your RAG system is working fine but it is still wrong. The retrieval fetches some chunks, passes it to…
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Agentic RAG: Let the Agent Search
application we build is a RAG app. The recipe is simple: chunk, embed, retrieve, then answer. It looks clean on paper. But once you use it on real cases, things…
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RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
, I have written quite a lot about RAG, starting with the Hitchhiker’s Guide to RAG with ChatGPT API and LangChain, and then exploring various topics related to RAG and…
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Proxy-Pointer RAG: Temporal Reasoning Without Semantic Precompilation
Retrieval-Augmented Generation (RAG) has evolved rapidly in recent times. The original RAG paradigm was designed to be straightforward: retrieve the most relevant chunks from a corpus and use them to…
