Stop eyeballing retrieval results — write real, repeatable tests that catch regressions the moment they happen.
1Why Manual Spot-Checks Fail Eventually
Manually trying a few queries and eyeballing whether the results 'look right' works fine the first time you build a pipeline. It fails the moment you change anything — a different chunk size, a different embedding model, a tweaked system prompt — because there's no record of what 'correct' looked like before, and no automatic way to notice it changed.
2The Shape of a Real Eval Suite
A retrieval eval suite is conceptually simple: a set of known queries paired with their expected correct result, run automatically, with a pass/fail count. Production RAG systems extend this same idea to generation quality too — checking that answers contain expected facts, correct citations, or avoid specific forbidden content — but it all starts from this exact pattern.
3Step-by-Step Breakdown
Module 5: Testing & Shipping. You've been eyeballing retrieval results this whole masterclass — 'yep, that looks right'. That doesn't scale, and it definitely doesn't catch a regression when you tweak your chunking strategy next month. This lesson writes real, repeatable tests against retrieve().
Write a Real Eval Suite. Three test cases, each pairing a known query vector with the chunk id it must retrieve. Finish the loop: compare the actual top result against the expected one, and count how many pass. This exact pattern is what a real CI pipeline would run on every change to your chunking or embedding logic.
Why write automated retrieval tests instead of just manually checking a few queries look reasonable each time you change the pipeline?
- →Automated tests run consistently on every change and catch silent regressions immediately — manual spot-checks are inconsistent, easy to skip under time pressure, and don't scale past a handful of queries.
- →Automated tests make the similarity search itself run faster.
Regressions, Caught Automatically. Now if you ever change your embeddings or chunking strategy and it breaks retrieval, this suite catches it immediately instead of a user reporting a wrong answer. Next: cutting real cost and latency with caching.
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Accessibility (A11y)
1Report Eval Failures With Actionable Detail
When an eval test fails, print or log which specific case failed and what was expected versus received, not just a pass/fail count — this is a developer-facing accessibility concern for debuggability.
print(f'FAIL: expected {expected}, got {actual}')SEO Implications
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Target 'RAG evaluation' and 'retrieval testing' as distinct, high-intent searches
Developers search for this specifically once they have a working RAG prototype and need to make it maintainable.
Best Practices
Run Your Eval Suite on Every Change to Retrieval or Embedding Logic
Treat retrieval accuracy as a testable property of your system, not a one-time manual verification — wire the eval suite into CI so any regression is caught before it reaches production.
Frequent Bugs
Only testing retrieval once during initial development, then never re-running the suite after later changes to chunking or the embedding model.
Re-run the eval suite on every meaningful pipeline change, and keep the test cases themselves version-controlled alongside the code.
Real-World Examples
Catching a Silent Regression
A team switches embedding models to cut cost, and their eval suite immediately fails 2 of 10 test cases — surfacing that the new model's vector space doesn't separate their document topics as cleanly, before any user ever saw a wrong answer.
assert similarity_search(query)[0] == expected_id