Day 59 of 60
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AI-system specific
RAG retrieval evaluation
Bad retrieval makes a good model hallucinate. Without precision, recall, and grounding metrics, RAG quality is folk wisdom dressed up as engineering.
ProblemRetrieval-augmented systems hallucinate when retrieval misses or returns garbage.
How it works
Measure retrieval precision, recall, MRR on a held-out set. Score generation against retrieved context (faithfulness, grounding).
What it catches
Retrieval misses, ungrounded generation, attribution drift. Without it, RAG quality is folk wisdom.
Tools
Ragas · OSS TruLens · OSS Phoenix · OSS
Verdict by project size
Small
Skip
Medium
Opt
Large
Rec
Extra-large
Must
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 1d | 1h | $0 | +1m |
| Medium 10–100k LOC | 3d | 5h | $0 | +3m |
| Large 100k–1M LOC | 15d | 30h | $500 | +10m |
| Extra-large >1M LOC | 50d | 120h | $5k | +20m |
Setup = engineer-days to first useful run ·
Maint = engineer-hours / month at steady state ·
Tool = out-of-pocket $ / month ·
CI = minutes added (or saved) per pipeline run
Lifecycle & ownership
When in lifecycle
Test Operate Observe
Per merge · Runs after merge to main; nightly heavy jobs.
Who owns it
ML / AI Engineer
Models, evals, drift, guardrails
Collaborates with: Developer, Security / AppSec
Reference implementations
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Ragas how-to guides
Retrieval and grounded-generation evaluation examples for RAG systems.
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TruLens examples
RAG quality, groundedness, and feedback-function examples.
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Arize Phoenix examples
RAG tracing and evaluation tutorials for retrieval diagnostics.
Quick check
Without RAG retrieval evaluation, RAG quality becomes…
One question. Pick the best answer. Your streak is saved locally on this device.
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