Who is this for, and how should you start?
For developers who can make basic model requests and want applications to answer from specified material with explainable sources and retrieval quality.
What is RAG and what does it solve?
RAG supplies retrieved material as model context to produce evidence-based answers. Define users and questions first, then identify documents that can answer them. Correctness is not automatic: verify document versions, passage relevance, permissions, and how the model uses evidence.
From documents to sourced answers
Keep document identifiers, sections, and versions, preserving conditions and meaning during chunking. Enforce permission filtering before retrieval and link citations to actual passages. Without sufficient evidence, explicitly abstain instead of presenting general model knowledge as a document conclusion.
How can a knowledge-base application be evaluated?
Prepare at least ten questions covering normal, cross-passage, unanswerable, and false-premise cases. Record retrieval, citation correctness, answers, and abstention separately. Classify missing material, chunking, retrieval, citation, and generation errors. Change one factor and retest the same set, preserving regressions.
Learn in sequence
4 learning units are available. Read, practice, and keep your verification records.
- Document processing and chunkingL3 · About 12 min
- Retrieval, citations, and answersL3 · About 15 min
- Evaluate retrieval and answer qualityL3 · About 18 min
- Failure analysis and improvementL3 · About 21 min
Test your methods in a project
From documents to evidence-based answers. Follow the project steps and verify actual deliverables.
Build your first RAG applicationCommon learning questions
Why can a retrieved document still lead to a wrong answer?
A passage may share keywords but omit a key condition. The supplied context or citation linkage may also be wrong. Inspect retrieved passages, actual model context, and generated output separately.
How should RAG projects be used in resumes and interviews?
Record the problem, personal contribution, implementation, tests, and failures. Support outcomes with actual measurements rather than invented accuracy or benefits. Prepare to explain tradeoffs and new constraints.