[FEATURE] LLM A/B Testing Framework — Systematic prompt and model comparison

[gelluisaac]

Description

Build a robust A/B testing framework for comparing prompts, models, and parameters
to systematically improve LLM performance.

Scope

Create statistical experimentation platform for LLM optimization.

Files to Touch/Create

• astroml/llm/experimentation/__init__.py
• astroml/llm/experimentation/ab_test.py — A/B test runner
• astroml/llm/experimentation/analyzer.py — Statistical analysis
• astroml/llm/experimentation/assigner.py — Traffic assignment
• astroml/llm/experimentation/reporter.py — Results reporting
• astroml/llm/experimentation/guardrails.py — Safety checks
• api/routers/experiments.py — Experiment API
• web/src/components/ExperimentDashboard.tsx — Dashboard UI

Experiment Types

1. Prompt Variants: Test different prompt phrasings
2. Model Comparison: GPT-4 vs Claude vs Llama
3. Parameter Tuning: Temperature, top_p, max_tokens
4. System Prompt: Different system instructions
5. Context Strategies: Different RAG approaches

Implementation Details

• Traffic splitting: 50/50, 90/10, etc.
• Minimum sample size calculation
• Statistical significance testing (t-test, chi-square)
• Confidence intervals
• Winner declaration automation
• Automatic rollback on regression

Acceptance Criteria

• Experiments run with proper randomization
• Statistical significance calculated correctly
• Results reproducible
• Winner deployed automatically when significant
• Safety metrics monitored during experiments
• Historical experiment data retained

Metrics Tracked

• Primary: task success rate, user satisfaction
• Secondary: latency, cost, token usage
• Safety: hallucination rate, toxicity

Labels

enhancement, llm, "experimentation", "optimization"