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.
Notes on retrieval-augmented generation, AI assistants and getting real answers out of your own documents.
How to write prompts that keep a RAG assistant answering from retrieved documents: block separation, positive instructions and traceable citations.
Stale indexes give confident wrong answers. How to set reindexing frequency by document tier, and what breaks when a sync run falls behind.
A retrieval augmented AI assistant answers new joiners' questions from your own documents, with a visible source for every answer. How to build one.
How to ground support answers in your own help center content, design the first reply, and hand over to a human before retrieval fails the customer.
Retrieval gaps are inevitable. Learn to classify three kinds of missing context and write refusals that keep users moving instead of guessing.
Vector search alone buries the right passage at rank 12. See how a cross-encoder reranker reorders retrieved chunks and fixes wrong RAG answers.
How to test a document-grounded assistant before launch: build the eval set from real questions, separate retrieval from generation, measure groundedness.
How to turn PDFs, scans and spreadsheets into searchable text: file triage, layout-aware extraction, OCR accuracy and clean chunks for a document bot.
How to enforce permissions in a RAG document assistant: ingestion, query-time and post-retrieval filtering, and how to model ACLs as chunk metadata.
Ragable indexes your files and answers from them, with citations. Start on SaaS or run it on your own infrastructure.
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