RoleGPSThe free map to your AI job
All capabilities · Retrieval & knowledge systems

Evaluate and improve retrieval quality

Build eval sets, measure recall/faithfulness (RAGAS-style), tune chunking, reranking, hybrid search.

~12 focused hoursintermediate
Explore 3 tools for this project
Market relevance

Which roles ask for this — and how often

Share of job postings in India, per role, that name this capability.

AI Quality Assurance / GenAI Test Engineer36%
What employers mean

You should be able to…

  1. Build a labeled eval set (questions + ground-truth answers/sources) for a RAG system
  2. Measure retrieval recall/precision and generation faithfulness (RAGAS-style metrics)
  3. Tune chunk size, overlap, and top-k based on eval scores, not guesswork
  4. Compare reranking on/off and hybrid vs vector-only retrieval with measured metrics
  5. Detect and reduce hallucination rate using faithfulness scoring
  6. Set up regression testing so RAG quality doesn't silently degrade after a change

Needs first: Build a grounded RAG application with citations

Learn — free, link-checked

The few resources that matter

Read · intermediate · 40 min · docs.smith.langchain.com

Evaluation

Shows how to build labeled datasets and run automated regression evals so RAG quality doesn't silently degrade. — LangChain
Read · intermediate · 40 min · docs.ragas.io

Ragas

Open-source metrics (faithfulness, answer relevancy, context precision) purpose-built for scoring RAG and agent outputs. — Ragas
Read · advanced · 40 min · huggingface.co

Advanced RAG on Hugging Face Documentation using LangChain

Covers reranking and query optimization plus a worked evaluation section, bridging pipeline-building and eval in one place. — Hugging Face
Course · advanced · 90 min · deeplearning.ai

Building and Evaluating Advanced RAG Applications

Pairs advanced retrieval techniques (sentence-window, auto-merging) directly with the eval metrics used to justify them. — DeepLearning.AI (Jerry Liu, Andrew Ng)
Tools for practice

Choose a tool for the job

Start with one tool for each part of your project. You don’t need to learn them all.

Go to the practice brief

3 tools to explore

Practices & references

  • Golden Q&A sets
  • Faithfulness and relevance

Selected for practice by RoleGPS. No paid placements or affiliate links.
Access can change; check the details before starting.

Think your tool is a good fit? Let us know

Practice

Eval harness for a RAG assistant: chunking-strategy shootout

Take the corpus and pipeline from your RAG project and hand-label 25 question / answer / source triples over it. Build a RAGAS-based (or equivalent) harness that computes faithfulness, answer relevance and context recall automatically instead of by eye. Run the same corpus through three different chunking strategies, report every metric per strategy, and prove the harness actually works by deliberately worsening a config and watching the scores fall.

Start from

Your own RAG corpus and pipeline from a previous project, plus 25 question/answer/source triples you hand-label over it

Milestones
  1. Hand-label the 25-triple eval set and commit it · ~1.5h
  2. Wire up the metrics and get one configuration scoring end to end · ~2.5h
  3. Re-index under three chunking strategies and score each one · ~2.5h
  4. Run the deliberate-regression check and write up the winner's tradeoffs · ~2.5h
Done when
  • A labeled eval set of at least 20 question/answer/source triples exists and is version-controlled
  • RAGAS or equivalent metrics (faithfulness, answer relevance, context recall) are computed automatically, not eyeballed
  • At least 3 chunking/retrieval configurations are compared with a results table showing which scores best on which metric
  • The eval harness can be re-run after a pipeline change to catch regressions
Prove it

Evidence a recruiter can check

  • A results table scoring three chunking strategies on faithfulness, answer relevance and context recall
  • The regression check: a deliberately worsened config with its lower scores next to the baseline's
  • The 25-triple labelled eval set committed, so every number in the table can be reproduced
  • The winning configuration argued on cost against quality, not just on the highest score
Signal it

Built a RAGAS-based eval harness for a RAG pipeline over a hand-labelled question set - compared three chunking strategies on faithfulness, relevance and context recall, and verified the harness catches a deliberately introduced regression.

Interview

Questions you'll get asked

  1. How do you build an evaluation set for a RAG system when you don't have pre-existing labeled data?
  2. What's the difference between faithfulness and answer relevance as RAG evaluation metrics?
  3. How would you use RAGAS or a similar framework to compare two chunking strategies objectively?
  4. How do you catch a regression in RAG quality before it ships, not after users complain?
  5. How would you measure whether a reranker is actually improving your pipeline's end results?
See where you stand for AI Quality Assurance / GenAI Test Engineer