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Since 2015
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RAG Over Messy PDFs: OCR, Structure-Preserving Chunking, and Incremental Re-indexing
Every RAG demo works on a clean PDF. Then you point it at a decade of scanned contracts and it returns confident nonsense. The chunk is the unit of retrieval, and three moves decide whether yours are any good before the model ever runs.
AI development
Agentic RAG: Why Retrieval Is the Layer Under the Agent
Classic RAG is a straight line: retrieve once, then answer. Agentic RAG is a control loop, an agent that decides what to fetch, whether it was enough, and whether the question is answerable at all. Here is the real graph, and when the loop is worth its cost.
AI development
How to Actually Evaluate Your RAG: Golden Sets, Retrieval Precision, and the Eval Loop
It feels better is not a number, and you cannot improve retrieval you cannot measure. How to build a golden set, measure the four things that matter, and run it as a regression so the next change to your RAG is a result, not a hunch.
AI development
Pinecone vs Qdrant vs pgvector: Choosing a Vector Database in 2026
There is no best vector database. There is the one that fits your hardest constraint. A practitioner who shipped one scores Pinecone, Qdrant, Weaviate, pgvector, Milvus, and Chroma on the questions that actually decide.
AI development
Your Agent Is Only as Smart as Its Search: Inside Our Hybrid RAG on Qdrant
Our matching agent answered a continuous stream of developer requests with confident, well-rounded, wrong shortlists, until we fixed the search under it. The architecture, the reasoning, and an interactive fusion demo you can tune yourself.
