#RAG

Capstone · DocQA — answer questions about your own documents

The final lesson ties the whole Go tutorial into one small product: an assistant that answers questions about your docs — grounded, cited, and refusing to guess.

The capstone: a grounded DocQA agent that answers only from embedded docs via a search_docs tool, cites sources, and refuses to guess — with an optional reviewer.

Rag

Grounding a Microsoft Agent Framework agent in your own docs with a search tool.

Ground a Microsoft Agent Framework agent with RAG-as-a-tool: expose a search function over your docs, instruct the agent to retrieve then answer and cite, and decline when nothing matches.

GraphRAG — when a knowledge graph beats vector search

Vector search treats every chunk as independent. GraphRAG models the relationships between entities, communities, and concepts. For corpus-spanning questions ("what's the relationship between X and Y"), graph wins.

HyDE — generate a hypothetical answer to improve retrieval

Embedding a question and embedding an answer often produce different vectors. HyDE generates a hypothetical answer to the question, embeds *that*, and retrieves on it. Retrieval quality goes up disproportionately.

Self-RAG and CRAG — when to retrieve, when to skip, when...

Naive RAG retrieves on every query. Self-RAG decides whether to retrieve. CRAG decides whether the retrieved content is good enough or needs corrective retrieval. Two papers; both worth implementing.

Multilingual RAG for India — Bhashini hooks and...

An Indian banking deployment needs to handle Hindi, Marathi, Tamil, Bengali, and English in the same retrieval pipeline. Bhashini (the government's language stack) plus cross-lingual embeddings make it tractable.