Chapter 8: Experimentation and Evaluation
Exam focus
- A/B testing
- Hyperparameter tuning
- Cross-validation
- Benchmarking
- Model comparison
- Latency measurement
- Throughput measurement
Scope Bullet Explanations
- A/B testing: Controlled comparison under realistic usage conditions.
- Hyperparameter tuning: Systematic search across training/inference settings.
- Cross-validation: Better reliability when data is constrained.
- Benchmarking/model comparison: Reproducible baseline-vs-candidate evaluation.
- Latency/throughput: Runtime gates that decide deployment viability.
Chapter overview
GENM evaluation is multi-dimensional: quality, robustness, cost, and speed. This chapter focuses on building decision-grade evaluation frameworks, not isolated benchmark scores.
Assumed foundational awareness
Expected baseline:
- metric definitions (precision/recall/F1),
- statistical significance intuition,
- experiment reproducibility basics.
Learning objectives
- Design robust experiments for multimodal model iteration.
- Compare candidates fairly with stable test conditions.
- Combine offline quality and online behavior evaluation.
- Apply runtime metrics as release gate criteria.
8.1 Experiment design patterns
A/B testing
Use for production-facing behavioral validation. Ensure traffic splitting, guardrail monitoring, and rollback criteria.
Hyperparameter tuning
Tune in structured sweeps with fixed seeds/config snapshots to maintain comparability.
Cross-validation
Useful when labeled multimodal data is limited or expensive.
8.2 Benchmarking and model comparison
Evaluation must lock:
- dataset version,
- prompt/template version,
- preprocessing version,
- scoring rubric.
Without this, model comparisons are not trustworthy.
8.3 Runtime evaluation
Latency and throughput should be measured across percentiles and load profiles, not average-only views.
Deployment decisions should include:
- quality gates,
- safety gates,
- latency SLO,
- throughput floor,
- cost ceiling.
8.4 Multimodal-specific evaluation concerns
Evaluate per modality and cross-modality interactions. A system can pass text metrics while failing image-grounding or speech-coherence requirements.
8.5 Release readiness framework
Use a scorecard that combines:
- functional quality,
- robustness under edge slices,
- runtime efficiency,
- safety/compliance checks.
Common failure modes
- Comparing models with different data snapshots.
- Optimizing for average latency and missing P95/P99 regressions.
- Ignoring modality-specific failure slices.
- Shipping without rollback triggers tied to runtime gates.
Chapter summary
Evaluation quality determines deployment quality. For GENM, reproducibility and multimodal slice analysis are essential to avoid misleading model improvements.
Mini-lab: release scorecard build
- Define baseline and candidate models.
- Create fixed evaluation dataset and rubric.
- Record quality + runtime metrics.
- Decide go/no-go with explicit gates.
Deliverable:
- one release scorecard with rationale and decision.
Review questions
- Why is A/B testing still needed after strong offline scores?
- What does cross-validation protect against in small datasets?
- Why must benchmarking lock data and prompt versions?
- What runtime metric pair is mandatory for serving decisions?
- Why can average latency be misleading?
- How do you include multimodal grounding quality in evaluation?
- What is one risk of unrestricted hyperparameter sweeps?
- Why should rollback criteria be defined before rollout?
- How do safety metrics integrate into release gates?
- What makes a model comparison scientifically defensible?
Key terms
A/B testing, cross-validation, benchmarking, release gate, percentile latency, throughput, rollback criteria.
Exam traps
- Relying on one benchmark or one metric.
- Ignoring runtime behavior under load variation.
- Shipping based on offline quality only.