Evals & Quality: Master LLM Evaluation

Evals & Quality: The Complete Guide to LLM Evaluation

Quality is non-negotiable. A single hallucination can destroy user trust. This track gives you the frameworks, metrics, and tools to ensure your LLMs perform reliably in production.

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🎯 What You’ll Master

Hallucination Detection

Identify and prevent factual errors, sycophancy, and logical inconsistencies before they reach users.

Model Drift Monitoring

Detect quality degradation early with PSI, output quality metrics, and continuous evaluation.

RAG Evaluation

Measure retrieval quality, faithfulness, and context relevance in your RAG pipelines.

Automated Testing

Build regression test suites and integrate quality gates into your CI/CD pipeline.

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🔍 Hallucination Detection & Prevention

Hallucination Types & Detection Practical detection methods

Hallucination Detector Benchmarks TLM vs RAGAS vs DeepEval

Prompt Engineering for Accuracy Reduce hallucinations by design

RAG for Grounding Use retrieval to reduce errors

Real-Time Hallucination Filtering Production safeguards

Fine-Tuning on Factuality Improve model reliability

Knowledge Cutoff Detection When training data becomes stale

Semantic Drift in RAG Context consistency checking

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📊 Model Drift & Quality Monitoring

Model Drift vs Data Drift Early detection frameworks

Population Stability Index (PSI) Input drift detection

Output Quality Metrics Beyond accuracy

A/B Testing LLMs Continuous model evaluation

Evaluation Frameworks DeepEval, RAGAS, OpenAI Evals

Feedback Loops Production data to improvement

Regression Testing Prevent quality drops

Root Cause Analysis When quality drops, find why

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📈 RAG & Advanced Evaluation

RAG Evaluation Metrics Faithfulness, relevance, precision

Context Relevance Are we retrieving the right content?

Reranking Quality Is reranking helping?

Multi-Hop Reasoning Complex reasoning evaluation

Benchmark Selection Choose the right eval datasets

Domain-Specific Evaluation Build evals that matter

Scale Evaluation Sampling and statistical significance

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🚀 Quick Wins

1. Implement structured outputs → 70% reduction in parsing errors
2. Add hallucination detection → Catch false claims before users see them
3. Set up drift monitoring → Early warning for quality degradation
4. Build a golden test set → Prevent regressions with every deploy

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Coming Soon: Interactive Hallucination Detector

Our hallucination detection playground with multiple detector comparison is under development.