On-Premise RAG: What You Give Up and What You Get Back
On-premise RAG keeps documents, index, embeddings and inference inside your boundary. What you lose in model choice, and what you gain in control.
Blog
Blog on RAG and AI assistants
Notes on retrieval-augmented generation, AI assistants and getting real answers out of your own documents.
Keeping a Knowledge Base Current Without a Full-Time Editor
How to keep a retrieval assistant's source documents fresh without hiring a full-time editor: spot rot, use usage data, and share the maintenance load.
Why a Document Assistant Still Makes Things Up, and How to Stop It
RAG assistants invent answers when retrieval fails first. How chunking, metadata and citation checks stop fabrication in document Q&A.
How to Make an AI Assistant Cite the Documents It Used
Reliable citations come from the retrieval layer, not the model. How to design chunk ids, anchors and answer formats so an AI assistant links to real...
Hybrid Retrieval: Combining Keyword Search With Embeddings
Why dense vector search alone fails on company documents, what BM25 still does better, and how hybrid retrieval merges both ranked lists into one.
Vector Database or Plain Keyword Search: When Each One Wins
Keyword search nails exact IDs, vector search handles vocabulary mismatch. A practical guide to picking the right retrieval method for your RAG project.
Choosing an Embedding Model for a Company Knowledge Base
How to pick an embedding model for a company knowledge base: start from your corpus, weigh hosted APIs against self-hosted weights, and measure retrieval.
Chunking Documents for Retrieval: Size, Overlap and Where to Cut
How chunk size and overlap decide retrieval quality, where to cut documents, and the failure modes that show up when boundaries are wrong.
What Retrieval Augmented Generation Actually Does for Your Documents
How retrieval augmented generation grounds answers in your own documents: ingestion, embedding, retrieval, generation, and why citations matter.
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