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Structured Test Suite Framework for LLM and Search Engine Upgrades

Structured Test Suite Framework for LLM and Search Engine Upgrades

 

I originally wrote this in early ’25, and it didn’t get much traction. Now that similar topics have been surfacing in various project discussions, it’s now in Confluence.

 

1. Introduction

This document outlines a structured test suite framework for evaluating and validating the transition of LLM models (e.g., Claude 3 to Claude 3.5) and search engine upgrades (e.g., AWS CloudSearch to OpenSearch). The framework ensures controlled, scalable, and reproducible evaluation using both test-collection-based measures and user-behavior-based analysis. Additionally, it is designed to be used for future code changes, such as configuration updates, ensuring that baseline functionalities are consistently validated as modifications occur. The metrics and implementation details in this document are based on common practices compiled by ChatGPT. They are furnished to provide ideas and spur brainstorming rather than serve as prescriptive solutions—they should be tailored to fit the specific needs of each organization and team.

 

2. Objectives

  • Provide a standardized test collection for benchmarking search and LLM models before deployment.

  • Enable modular evaluation so backend changes can be tested without overburdening the frontend.

  • Establish scalable, repeatable methodologies that ensure high-quality upgrades without degrading user experience.

  • Combine test-collection-based evaluation with real-world user behavior monitoring for a balanced assessment.

 

3. Test-Collection-Based Evaluation (Pre-Deployment Testing)

3.1 LLM Model Upgrade Evaluation

Test Collection Setup:

  • Curate a representative dataset of prompts covering key use cases.

  • Define gold-standard responses (human-labeled or reference outputs from prior models).

Evaluation Metrics:

  • Perplexity & BLEU Score: Measures coherence and fluency.

  • NDCG & MRR: Evaluates ranking-based outputs.

  • Factual Accuracy & Hallucination Rate: Validates correctness and reliability.

  • Response Latency: Measures time to generate responses.

Automated Testing Pipeline:

  • Run test prompts on both the old and new LLM versions.

  • Compare outputs against reference answers using defined metrics.

  • Identify regressions and optimize before A/B testing with users.

3.2 Search Engine Upgrade Evaluation

Test Collection Setup:

  • Define a static dataset of queries and relevance-labeled documents.

  • Ensure queries represent a mix of real-world search intents.

Evaluation Metrics:

  • Precision@K: Measures percentage of relevant results in top K.

  • Recall: Ensures all relevant documents are retrieved.

  • NDCG (Normalized Discounted Cumulative Gain): Evaluates ranking quality.

  • Mean Reciprocal Rank (MRR): Determines how soon the first relevant result appears.

  • Query Latency: Measures search response time.

Automated Testing Pipeline:

  • Execute the test queries on both CloudSearch and OpenSearch.

  • Analyze ranking effectiveness and response times.

  • Identify and mitigate indexing, ranking, or latency regressions before live deployment.

 

4. User-Behavior-Based Evaluation (Live A/B Testing)

4.1 LLM User Behavior Testing

Key Metrics:

  • Acceptance Rate: How often users accept AI-generated responses.

  • Correction Rate: Frequency of users modifying or rejecting responses.

  • User Engagement (Time-on-Task): Measures interaction duration with generated responses.

  • User Satisfaction (Explicit Feedback): Direct ratings or survey-based satisfaction data.

A/B Test Implementation:

  • Split 50% of traffic to the old LLM model, 50% to the new model.

  • Collect engagement metrics and compare effectiveness.

  • Iterate based on user satisfaction before full deployment.

4.2 Search Engine A/B Testing

Key Metrics:

  • Click-Through Rate (CTR): Measures engagement with search results.

  • Search Abandonment Rate: Indicates frustration when no results are clicked.

  • Search-to-Download Conversion: Tracks if users find datasets valuable enough to download.

  • Query Reformulation Rate: Detects issues in relevance where users refine queries.

A/B Test Implementation:

  • Route 50% of queries to CloudSearch, 50% to OpenSearch.

  • Compare user behavior between the two engines.

  • Optimize ranking parameters based on real-world engagement data.

 

5. Modular Benchmarking for Backend-Only Adjustments

Key Considerations:

  • Separation of Concerns: Ensure backend search engine or LLM improvements do not disrupt frontend teams unnecessarily.

  • Reproducible Test Collections: Maintain curated query-response datasets for scalable benchmarking.

  • Automation-First Approach: Leverage CI/CD pipelines for automated LLM and search testing before A/B testing.

Benefits:

✅ Faster iterations without affecting live users. 

✅ Easier debugging and controlled rollout. 

✅ Scalable benchmarking methodology adaptable to future upgrades.

 

 

6. Transition Plan from Test Collection to Hybrid Evaluation

Phase

Testing Approach

Goal

Phase 1

Test-Collection-Based Evaluation

Ensure foundational accuracy, ranking quality, and performance

Phase 2

A/B Testing with Limited Traffic

Validate in real-world use cases with user engagement data

Phase 3

Full Deployment with Continuous Monitoring

Optimize post-deployment using user behavior insights

 

7. Next Steps

  1. Curate test datasets for LLM and search queries.

  2. Establish automated evaluation pipelines for benchmarking.

  3. Run pre-deployment tests and refine models before user testing.

  4. Gradually roll out A/B testing with live users.

  5. Iterate based on behavioral insights and finalize full deployment.

 

Ref.,

Search Test Data Submission Guideline