Prompt Design for Grounded Answers Instead of Creative Ones
How to write prompts that keep a RAG assistant answering from retrieved documents: block separation, positive instructions and traceable citations.
Evaluating a RAG System Before You Let Anyone Use It
How to test a document-grounded assistant before launch: build the eval set from real questions, separate retrieval from generation, measure groundedness.
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.
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.
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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