Experimentation and Iteration
The Build-Measure-Learn Loop
For AI Applications
[Hypothesis] → [Build/Change] → [Deploy] → [Measure] → [Learn] → [Next Hypothesis]
Types of Experiments
Prompt Experiments
- Test different system prompts
- Compare few-shot examples
- Try varied output formats
- Adjust temperature/parameters
Model Experiments
- Compare base models
- Test fine-tuned versions
- Evaluate quantized variants
- Try different architectures
Architecture Experiments
- With/without RAG
- Agent vs direct call
- Caching strategies
- Routing approaches
Experiment Tracking
Key Metrics to Log
| Category | Metrics |
|---|---|
| Quality | Accuracy, human pref, LLM-as-judge |
| Performance | Latency, throughput |
| Cost | $/request, tokens used |
| Safety | Guardrail violations |
Tools
| Tool | Type | Best For |
|---|---|---|
| Weights & Biases | Commercial | ML experiments |
| MLflow | Open source | Model tracking |
| LangSmith | Commercial | Prompt experiments |
| Langfuse | Open source | LLM tracing |
Feedback Loop Integration
User Feedback Collection
@app.post("/feedback")
def collect_feedback(request_id: str, thumbs_up: bool, comment: str = None):
log_feedback(request_id, thumbs_up, comment)
**Use for fine-tuning or prompt improvement**
Automated Learning 1. Collect user feedback (thumbs up/down) 2. Identify low-rated responses 3. Analyze patterns 4. Update prompts or fine-tune 5. Measure improvement
Best Practices
- Change one variable at a time
- Use statistical tests for significance
- Document all experiments
- Version prompts like code
- Create experiment templates for reproducibility
experimentiteratefeedback loop
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