...answer questions from my data

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applies to every path

YouTube ... Quora ...Google search ...Google News ...Bing News

Use this page when people should be able to ask questions and get answers grounded in your information: policies, manuals, research, tickets, contracts, sales data. The model supplies language skills; you supply the facts at question time, which keeps answers current and lets you cite sources.

Start here

  • Where does the knowledge live?
    • In databases or spreadsheets → text-to-SQL or a semantic layer: the LLM writes a query, your database answers. Use read-only access, curated views and validated SQL. Many data platforms now ship this built in → Database; Vector & Relational
    • In documents (PDFs, wikis, tickets, email)
      • Small and stable (fits in the model's context window, roughly a few hundred pages) and modest traffic → long-context prompting: put the documents in the prompt and use prompt caching to cut cost
      • Larger or changing, or you need citations or per-user permissions → RAG: parse → split into chunks → embed → hybrid search (keywords + vectors) → rerank → answer with citations → RAG
      • Questions about relationships or "across all documents" themes → knowledge graph + RAG (GraphRAG) → Graph
      • Multi-step questions over many sources → agentic RAG or deep-research agents that search, read and search again → ...build an agent
    • In live business systems (CRM, ticketing, ERP) → tool calls or MCP connectors that fetch current records → ...build an agent
  • Need it working this week without building? → enterprise assistants with connectors and permissions (Microsoft 365 Copilot, Gemini Enterprise, ChatGPT Enterprise, Claude Enterprise, Glean); for personal research, notebook-style tools such as NotebookLM
  • Building it yourself? → frameworks (LlamaIndex, LangChain, Haystack); vector search (pgvector in Postgres, Elasticsearch/OpenSearch, Pinecone, Weaviate, Qdrant, Milvus, Chroma); managed services (Azure AI Search, Google Cloud's agent platform search, Amazon Bedrock Knowledge Bases)

Quick picks

Situation Try first Then add
A handbook or a few contracts Long context + prompt caching RAG when it outgrows the context window
Thousands of documents Hybrid search + reranker + citations Better parsing and chunking, metadata filters
"Which teams mention X across all reports?" GraphRAG Agentic RAG
Sales or finance tables Text-to-SQL on curated views A semantic / metrics layer
Must respect who can see what RAG with document-level permissions Per-user connectors

Real-world examples

Watch out

  • Retrieval is usually the weak link — poor parsing (tables, scans) and bad chunking cause most wrong answers.
  • Permissions — never retrieve documents the user is not allowed to see.
  • Stale indexes — re-index when sources change; show document dates.
  • Prompt injection — retrieved text can contain hidden instructions; treat it as data.

Measure success

Retrieval recall@k and MRR, groundedness (is every claim supported by the retrieved text?), answer relevance, citation accuracy, and correct "I don't know" answers when the information is missing. More: ...evaluate and trust AI

Learn more

Where next? ◀ Generate & converse · ...understand language · ...build an agent · ...customize a model · ▶ ...evaluate and trust AI
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