Did Your RAG Answer the Whole Question? Measuring Context Coverage
Complex prompts have multiple angles. When your vector search only finds context for half the question, your users get half-baked answers. Here is the recipe to catch it.
The Reality Check
80% of enterprise queries aren't simple trivia; they are comparative or multi-step questions. If you don't evaluate context coverage, your RAG app will silently drop half the user's intent.
The Silent Failure of Comparative & Multi-Part Prompts
Users ask: 'Compare our SOC2 compliance requirements for AWS vs Google Cloud environments.' Standard semantic search matches AWS documents with high similarity, fills the top-k slots, and completely omits Google Cloud chunks.
Real-World Scenario
"An engineer asks the dev assistant: 'How do I migrate our postgres database and what are the rollback steps if migration fails?' The retrieved chunks detail migration commands, but zero rollback steps. The engineer proceeds without a safety net."
The Hidden Costs of Leaving This Unchecked:
- Users having to repeat their prompts multiple times ('You didn't answer my second question!').
- Loss of executive confidence in AI rollout initiatives.
- Production incidents caused by missing rollback and contingency instructions.
Full Coverage Across All Query Facets
Context Coverage measures the proportion of needed information facets present across the gathered context passages.
Prevented
Unbalanced multi-topic context retrieval.
Prevented
Silent omission of caveats, exceptions, and procedural steps.
Prevented
Single-source bias in comparative evaluations.
Loop for More Context When Coverage Is Incomplete
Run ContextCoverage on your retrieved bundle. If coverage is below 3.0/5.0, perform query decomposition: break the original prompt into sub-queries, fetch missing facets, and merge.
04 · Value Proposition
The LayaEval Advantage
Advantage 01
Actionable Facet Scoring
Know whether you have full 5.0 coverage or partial 2.0 coverage in milliseconds.
Advantage 02
Adaptive Retrieval Enabler
Use the score to dynamically trigger multi-query generation or HyDE only when necessary.
Advantage 03
Pure Python Simplicity
Zero heavy external orchestration frameworks required. Works directly with your existing code.
Run This Check in 4 Lines of Python
By catching that the Pro plan storage limits were absent, you avoided generating an incomplete response that would have frustrated your customer.
1from layaeval import ContextCoverage2
3evaluator = ContextCoverage(threshold=3.0)4
5query = "Compare pricing and storage limits between Starter and Pro plans."6context = "Starter plan costs $10/mo with 50GB storage. Pro plan costs $25/mo."7
8result = evaluator(query=query, retrieved_context=context)9
10print(f"Coverage Passed: {result.passed}") # False (Missing Pro storage limits!)11print(f"Coverage Score: {result.value:.1f}/5.0") # e.g. 2.412
13if not result.passed:14 # Dynamically expand search for the missing piece:15 additional_context = fetch_missing_context(query="Pro plan storage limit")16 context += " " + additional_contextAPI Payload & Evaluation Response
Query your evaluation service to verify context coverage before drafting multi-step answers:
Request (cURL / POST)
1curl -X POST https://api.yourdomain.com/v1/eval/context-coverage \2 -H "Content-Type: application/json" \3 -d '{4 "query": "Compare pricing and storage limits between Starter and Pro plans.",5 "retrieved_context": "Starter plan costs $10/mo with 50GB storage. Pro plan costs $25/mo."6 }'Instant Sub-10ms JSON Response
1{2 "metric": "ContextCoverage",3 "passed": false,4 "score": 2.4,5 "threshold": 3.0,6 "missing_aspects_detected": true7}- 1Track average ContextCoverage across all production queries.
- 2Set an automated alert when coverage dips below 3.0 on key product categories.
- 3Pair with query-expansion sub-agents to automatically fetch missing facets.
Interactive Playground
Test Context Coverage in our visual demo
Tweak query and context sliders to see live pass/fail scoring in real time.
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