Workflow playbook

RAG knowledge base evaluation workflow

Evaluate a RAG knowledge base by testing ingestion quality, source retrieval, answer faithfulness, and update ownership before scaling infrastructure.

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Target users

  • AI builders
  • Product engineers
  • Knowledge teams

Inputs

  • Document set
  • Representative questions
  • Expected sources
  • Answer quality rules

Outputs

  • Retrieval scorecard
  • Ingestion fixes
  • RAG launch decision

Boundaries

  • Do not treat model fluency as retrieval quality.
  • Keep source documents, chunks, and metadata reviewable.
  • Avoid production RAG until update and deletion rules are owned.

Common mistakes

  • Choosing a vector database before writing real test queries.
  • Judging RAG quality only by fluent answers instead of retrieved sources.
  • Ignoring document update rules, deleted content, and metadata ownership.

Templates

  • RAG retrieval scorecard
  • Knowledge ingestion review sheet

Primary tools

LlamaIndexData and RAG framework for knowledge-heavy AI applications.LangChainAgent engineering framework and observability platform.PineconeManaged vector database for RAG, semantic search, and AI assistants.WeaviateAI-native vector database with free cloud and deployment flexibility.OpenAI APIGeneral-purpose model APIs for product builders.

Alternatives

CohereEnterprise AI platform for Command, Embed, Rerank, and RAG systems.FirecrawlWeb data API for search, scraping, crawling, and agent context.ApifyActor platform for web scraping, automation, and AI agent data.ClaudeLong-context assistant for writing, analysis, and coding workflows.

Steps

  1. 1

    Create retrieval fixtures

    Collect real questions and mark the source passages that should answer them.

    Output: RAG evaluation fixture set.

    Claude
  2. 2

    Test ingestion and retrieval

    Run retrieval tests against chunks, metadata, filters, and expected source coverage.

    Output: Retrieval quality report.

    LlamaIndexLangChainPineconeWeaviate
  3. 3

    Review generated answers

    Check whether answers cite the right sources, avoid unsupported claims, and handle unknowns safely.

    Output: Answer faithfulness review.

    OpenAI APICohereClaude

Copyable prompts

Create a RAG evaluation set with user questions, expected sources, metadata filters, and failure cases.

Review these retrieved chunks and answers for source mismatch, unsupported claims, and missing fallback behavior.

Related tools

LlamaIndexData and RAG framework for knowledge-heavy AI applications.LangChainAgent engineering framework and observability platform.PineconeManaged vector database for RAG, semantic search, and AI assistants.WeaviateAI-native vector database with free cloud and deployment flexibility.OpenAI APIGeneral-purpose model APIs for product builders.CohereEnterprise AI platform for Command, Embed, Rerank, and RAG systems.

Related guides

How small teams should choose a RAG stackA practical guide to choosing embeddings, vector search, retrieval evaluation, data ingestion, and model APIs for small-team RAG systems.How to choose AI research tools for source-backed decisionsA selection guide for choosing research assistants, search APIs, scraping tools, and synthesis models without confusing summaries with evidence.Model API selection framework for AI product buildersA method for comparing model APIs by task fit, quality, latency, cost, privacy, and fallback strategy.

Use cases

  • RAG prototype
  • Knowledge assistant
  • Internal search quality review