experiment
**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**
```python
@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