Reranking Retrieved Passages: The Step Most Teams Skip
Vector search alone buries the right passage at rank 12. See how a cross-encoder reranker reorders retrieved chunks and fixes wrong RAG answers.
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.
Turning PDFs, Scans and Spreadsheets Into Text a Bot Can Search
How to turn PDFs, scans and spreadsheets into searchable text: file triage, layout-aware extraction, OCR accuracy and clean chunks for a document bot.
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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