The outcome

Replace anecdotal RAG testing with repeatable evidence for relevance, faithfulness, latency, and cost.

LlamaIndex is a open-source data framework for retrieval-augmented generation and agent workflows. Loading and indexing private data, building source-grounded retrieval applications, composing workflows and agents, and evaluating retrieval and response quality. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Create a representative dataset with queries, expected source IDs, reference answers or criteria, and difficult no-answer cases.

  2. 02

    Measure retrieval with appropriate ranking metrics and inspect false positives, false negatives, permission errors, and stale documents.

  3. 03

    Measure response faithfulness, relevance, correctness, citation support, refusal behavior, latency, and model or embedding cost.

  4. 04

    Trace loading, indexing, retrieval, reranking, prompt construction, and generation so each failed example has a diagnosable stage.

  5. 05

    Set release thresholds, compare changes against a fixed baseline, review evaluator disagreements, and monitor sampled production traffic safely.

Working standard

What good use looks like.

  • Keep a fixed regression set.
  • Separate retrieval and answer metrics.
  • Review automated evaluator failures manually.

RAG can retrieve irrelevant, stale, private, or malicious content and still produce fluent answers. Enforce access during retrieval, sanitize sources, preserve citations, separate retrieval and response evaluation, and protect traces and model credentials.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.