Generative AI & LLM

Module 13 of 16

Module 13: Evaluation and Benchmarking

3 min read517 words
What you'll learn
Identify the unique challenges of evaluating LLM outputsImplement RAGAS metrics for RAG pipeline evaluationBuild LLM-as-judge evaluation pipelinesDesign and run A/B tests for LLM applicationsCreate continuous evaluation systems for production monitoring

Duration: 4 hours | Difficulty: Intermediate–Advanced | Prerequisites: Modules 01–06

Learning Objectives

By the end of this module, you will be able to:

  • Identify the unique challenges of evaluating LLM outputs
  • Implement RAGAS metrics for RAG pipeline evaluation
  • Build LLM-as-judge evaluation pipelines
  • Design and run A/B tests for LLM applications
  • Create continuous evaluation systems for production monitoring

1. Why LLM Evaluation is Hard

Unlike assert add(2,2)==4, “good summary?” is subjective, contextual, and sampler-dependent. You need multiple signals: human labels, automatic proxies, and product metrics.

┌─────────────────────────────────────────┐
│         LLM Evaluation Methods           │
│                                          │
│  ┌────────────┐  ┌───────────────────┐  │
│  │ Automated   │  │ Human Evaluation  │  │
│  │ ┌─────────┐│  │  ┌─────────────┐ │  │
│  │ │Reference││  │  │ Expert review│ │  │
│  │ │-based   ││  │  │ User studies │ │  │
│  │ │(BLEU,   ││  │  │ A/B testing  │ │  │
│  │ │ROUGE)   ││  │  └─────────────┘ │  │
│  │ └─────────┘│  └───────────────────┘  │
│  │ ┌─────────┐│                          │
│  │ │LLM-as-  ││  ┌───────────────────┐  │
│  │ │Judge    ││  │ RAG-Specific       │  │
│  │ │         ││  │ (RAGAS, DeepEval)  │  │
│  │ └─────────┘│  └───────────────────┘  │
│  └────────────┘                          │
└─────────────────────────────────────────┘
TrapFix
Eval set = training paraphrasesFresh prompts, adversarial set
Single numeric scoreDashboard of metrics
Judge model = same as subjectUse stronger / different model cautiously

Concept: Evaluation is a product discipline, not a one-off notebook cell.

2. RAGAS: RAG Evaluation

RAGAS scores faithfulness, answer relevancy, context precision/recall—useful sanity checks when you have reference answers.

Key Example: Minimal evaluate call—expand the dataframe before trusting headline numbers.

python
[object Object],
,[object Object], ragas ,[object Object], evaluate
,[object Object], ragas.metrics ,[object Object], faithfulness, answer_relevancy, context_precision, context_recall
,[object Object], datasets ,[object Object], Dataset

eval_data = {
    ,[object Object],: [,[object Object],, ,[object Object],],
    ,[object Object],: [
        ,[object Object],,
        ,[object Object],,
    ],
    ,[object Object],: [
        [,[object Object],],
        [,[object Object],],
    ],
    ,[object Object],: [
        ,[object Object],,
        ,[object Object],,
    ],
}
ds = Dataset.from_dict(eval_data)
results = evaluate(ds, metrics=[faithfulness, answer_relevancy, context_precision, context_recall])
,[object Object],(results)

Understanding RAGAS Metrics

MetricMeasuresAim high when…
FaithfulnessClaims supported by contextMedical/legal-ish
Answer relevancyAddresses questionCustomer support
Context precisionRetrieved noise levelBig corpora
Context recallMissing gold factsCompliance Q&A

Fun Fact: RAGAS itself uses LLM calls—budget for eval cost like you budget for prod.

3. LLM-as-Judge

Use a strong model with a rubric (“rate 1–5 on accuracy, safety, concision”) and blind presentation when comparing candidates.

Good practiceBad practice
Show reference answer if availableLet judge see model names
Average multiple judge samplesSingle roll decides launch

4. Building an Evaluation Pipeline

StageArtifact
Curateeval_set.jsonl with tags
RunBatch script + structured logs
AnalyzeFailure taxonomy dashboard
GateCI job blocking regressions

Plain-English loop: nightly run on golden set → Slack alert if faithfulness drops > X%.

5. A/B Testing LLM Systems

Route traffic to A/B prompts or models; measure task success, not just CTR. Watch latency and cost—a “smarter” model might lose on economics.

MetricTooling
Win rate on thumbsProduct analytics
Business KPIWarehouse / BI

Key Takeaway

Ship eval hooks before you ship the feature flag.

Practice Exercises

Exercise 1: Golden Set Curation (Beginner)

50 questions with must-cite chunk IDs.

Exercise 2: RAGAS on Your Corpus (Intermediate)

Run full metrics; inspect worst rows.

Exercise 3: Judge Rubric Design (Intermediate)

Write anchored 1–5 scale; test inter-rater on 20 rows.

Exercise 4: CI Eval Job (Advanced)

GitHub Action fails PR if metric regresses.

Exercise 5: Online Monitoring (Advanced)

Sample prod traffic to shadow eval pipeline.

Mini-Project: LLM Evaluation Dashboard

Streamlit or Grafana page: latest RAGAS run, latency percentiles, cost per 1k queries, top failure tags.

Key Takeaways

Key Takeaway

  • There is no single “accuracy” for generative apps—use suites.
  • RAGAS gives fast RAG health checks; still spot-check manually.
  • LLM judges need rubrics, blindness, and variance control.
  • Pipelines + CI beat quarterly manual reviews.
  • A/B tests tie model changes to outcomes stakeholders understand.

Resources for Further Learning

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