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Best Practices for AI Development

Essential best practices for building production-ready AI applications.

Model Selection

Choose the Right Model

For Production: - ✅ Use proven, stable models (GPT-4, Claude, Llama 2) - ✅ Consider cost vs performance tradeoffs - ✅ Test on your specific use case

For Experimentation: - ✅ Try latest models from Foundation Models - ✅ Benchmark on standard tasks - ✅ Document findings

Prompt Engineering

Write Effective Prompts

# ❌ Bad: Vague
prompt = "Write something about AI"

# ✅ Good: Specific
prompt = """Write a 200-word introduction to AI for beginners.
Include:
1. Definition
2. Current applications
3. Future potential"""

Learn more: Prompt Engineering

RAG Implementation

Build Robust RAG Pipelines

  1. Chunk Wisely
  2. Size: 512-1000 tokens
  3. Overlap: 10-20%

  4. Preserve context

  5. Choose Quality Embeddings

  6. OpenAI: Best quality, costs $
  7. BGE: Free, good quality

  8. Match to your domain

  9. Optimize Retrieval

  10. Use hybrid search (semantic + keyword)
  11. Implement reranking
  12. Cache frequent queries

See: RAG & Knowledge

Production Deployment

Deploy Safely

Before Production: - ✅ Comprehensive testing (Evaluation & Testing) - ✅ Set up monitoring (Observability) - ✅ Implement guardrails (AI Safety) - ✅ Plan for scaling (Production AI)

Deployment Checklist: - [ ] Load testing completed - [ ] Error handling implemented - [ ] Logging configured - [ ] Alerts set up - [ ] Rollback plan ready - [ ] Documentation updated

Cost Optimization

Reduce Expenses

  1. Choose Right Model Size
  2. Use GPT-3.5 where GPT-4 isn't needed

  3. Try open-source models

  4. Optimize Context

  5. Don't send full conversation history
  6. Summarize older messages

  7. Use sliding windows

  8. Cache Aggressively

  9. Cache common queries
  10. Use semantic similarity matching

  11. Set appropriate TTLs

  12. Batch Requests

  13. Process multiple items together
  14. Use async/parallel execution

Security

Protect Your Application

Input Validation: 

# Check for prompt injection
if is_injection_attempt(user_input):
    return safe_refusal()

# Sanitize inputs
clean_input = sanitize(user_input)

Output Filtering: 

# Check for harmful content
if contains_harmful_content(response):
    return filtered_response()

# Redact PII
response = redact_pii(response)

Learn more: AI Safety & Alignment

Monitoring

Track Key Metrics

Performance: - Latency (p50, p95, p99) - Throughput (requests/sec) - Error rate

Quality: - User satisfaction - Response relevance - Hallucination rate

Cost: - Tokens per request - Cost per user/day - Monthly burn rate

See: Observability & Monitoring

Testing

Test Thoroughly

Unit Tests: 

def test_prompt_generation():
    prompt = generate_prompt("test query")
    assert len(prompt) > 0
    assert "test query" in prompt

Integration Tests: 

def test_rag_pipeline():
    result = rag_pipeline.query("What is AI?")
    assert result.answer is not None
    assert len(result.sources) > 0

Regression Tests: 

# Ensure quality doesn't degrade
def test_against_golden_set():
    for test_case in golden_dataset:
        score = evaluate(model, test_case)
        assert score >= QUALITY_THRESHOLD

More: Evaluation & Testing

Data Management

Handle Data Properly

Training Data: - ✅ Clean and deduplicate - ✅ Remove PII - ✅ Balance datasets - ✅ Version control

Evaluation Data: - ✅ Separate from training - ✅ Representative of production - ✅ Regularly updated - ✅ Never modify after creation

See: Datasets & Data Tools

Code Quality

Write Maintainable Code

Structure: 

project/
├── src/
│   ├── models/       # Model interfaces
│   ├── prompts/      # Prompt templates
│   ├── chains/       # LLM chains
│   └── utils/        # Helpers
├── tests/            # Test suite
├── config/           # Configuration
└── docs/             # Documentation

Documentation: - Document prompts and why they work - Explain model selection decisions - Note failure modes and workarounds

Continuous Improvement

Iterate and Improve

  1. Collect Feedback
  2. User ratings
  3. Bug reports

  4. Feature requests

  5. Analyze Metrics

  6. Identify bottlenecks
  7. Find common failures

  8. Track costs

  9. A/B Test Changes

  10. Test new prompts
  11. Try different models

  12. Optimize parameters

  13. Document Learnings

  14. What worked
  15. What didn't
  16. Why

Resources

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