Retrieval CompletenessRAG EvaluationBenchmark & Regression Essential4 min read

Did Your Search Find All the Big Evidence? Testing Retrieval Completeness

High semantic similarity scores are deceitful: they can be 0.95 while leaving out the pivotal paragraph that changes everything. Here is how to audit retrieval completeness.

Published October 26, 2026·LayaEval Engineering

View interactive Retrieval Completeness scene

The Reality Check

Are you running RAG evaluations without testing whether key evidence was fetched in the first place? If search missed the primary evidence, no prompt engineering on earth will save your answer.

01 · Business Problem

The Illusion of High Retrieval Scores

Teams often celebrate because their vector database returned 5 chunks with high cosine similarity. But if the single most definitive document was ranked #6 or filtered out by chunking boundaries, your system fails silently.

Real-World Scenario

"In an insurance claim appraisal RAG, an adjuster asks whether water damage from roof leaks is covered. The system retrieves 4 chunks describing general water damage coverage, but misses the crucial policy rider exclusion for deferred roof maintenance. The insurer erroneously approves a $30,000 ineligible claim."

The Hidden Costs of Leaving This Unchecked:

False sense of security from high vector similarity metrics that ignore recall.
Costly downstream errors in legal, insurance, medical, and mission-critical workflows.
Endless prompt engineering cycles when the actual defect is in search recall.

02 · What We Solve

Guaranteed Evidence Retrieval for Critical Workflows

Retrieval Completeness tests whether search successfully brought back all core pieces of evidence needed for an authoritative verdict.

Prevented

Missing policy exclusions, critical riders, and safety prerequisites.

Prevented

Under-retrieval caused by improper chunk sizes or poor embedding models.

Prevented

Silent regression after rebuilding search indices or upgrading embedding versions.

03 · The Solution

Add RetrievalCompleteness to Your CI/CD & Regression Suite

Run golden benchmark evaluations with RetrievalCompleteness in your test pipeline (`pytest`). Whenever someone tweaks chunk size, overlap, embedding models, or reranking weights, verify that all core evidence continues to be retrieved.

Rule of Thumb: Test retrieval completeness in CI before your customers test it in production.

04 · Value Proposition

The LayaEval Advantage

Advantage 01

Native CI/CD Integration

Simple python assertion: `assert result.passed` in your GitHub Actions or GitLab pipelines.

Advantage 02

Sub-10ms Speed

Run 500 regression test cases in seconds, not hours.

Advantage 03

Clear Pass/Fail Criteria

Passes when nothing big is missing; fails immediately when key evidence is absent.

05 · Python Implementation

$pip install layaeval

Run This Check in 4 Lines of Python

By including the exclusion clause in the retrieved context, the check passes with flying colors, guaranteeing your system will not overlook crucial exclusions.

verify-vector-search-recall-retrieval-completeness.py

1import pytest
2from layaeval import RetrievalCompleteness
3
4evaluator = RetrievalCompleteness(threshold=3.0)
5
6def test_water_damage_claim_retrieval():
7    query = "Is interior ceiling damage from a neglected roof leak covered?"
8    
9    # Simulate your retrieval pipeline output
10    retrieved_chunks = (
11        "Section 4: Water damage from sudden burst pipes is fully covered. "
12        "Section 9.2 Exclusion: Water penetration resulting from lack of normal "
13        "roof maintenance, aging shingles, or deferred repairs is explicitly excluded."
14    )
15    
16    result = evaluator(query=query, retrieved_context=retrieved_chunks)
17    
18    # Assert in CI/CD pipeline
19    assert result.passed, f"Retrieval failed completeness: {result.value}/5.0"
20    print(f"Completeness verified! Score: {result.value}")

06 · REST API & Microservice Pattern

API Payload & Evaluation Response

Automate retrieval quality audits via API in your data ingestion pipelines:

Request (cURL / POST)

request.sh

1curl -X POST https://api.yourdomain.com/v1/eval/retrieval-completeness \
2  -H "Content-Type: application/json" \
3  -d '{
4    "query": "Is interior ceiling damage from a neglected roof leak covered?",
5    "retrieved_context": "Section 4: Water damage... Section 9.2 Exclusion..."
6  }'

Instant Sub-10ms JSON Response

response.json

1{
2  "metric": "RetrievalCompleteness",
3  "passed": true,
4  "score": 4.9,
5  "threshold": 3.0,
6  "confidence": 0.97,
7  "evidence_complete": true
8}

07 · Actionable Implementation Checklist

1
Create a golden dataset of your 50 most critical business queries.
2
Run RetrievalCompleteness in CI on every knowledge base deployment.
3
Never deploy a new embedding model without benchmarking against this check.

Interactive Playground

Test Retrieval Completeness in our visual demo

Tweak query and context sliders to see live pass/fail scoring in real time.

Open Interactive Scene

Did Your Search Find All the Big Evidence? Testing Retrieval Completeness

The Illusion of High Retrieval Scores

Guaranteed Evidence Retrieval for Critical Workflows

Add RetrievalCompleteness to Your CI/CD & Regression Suite

The LayaEval Advantage

Native CI/CD Integration

Sub-10ms Speed

Clear Pass/Fail Criteria

Run This Check in 4 Lines of Python

API Payload & Evaluation Response

Test Retrieval Completeness in our visual demo

More in the RAG Cookbook

How to Stop RAG Context Pollution with Retrieval Relevance

Did Your RAG Answer the Whole Question? Measuring Context Coverage