3.8. Reflection Checkpoint

Key Takeaways

Before proceeding, ensure you can:

• Configure chat completions with appropriate temperature and max_tokens for your use case
• Implement DALL-E image generation knowing that only prompt is required
• Distinguish between RAG (adds knowledge) and fine-tuning (changes behavior/style)
• Use prompt templates with Jinja2 syntax ({{ variable }} for values, {% %} for logic)
• Enable tracing with OpenTelemetry and Azure Monitor
• Deploy containers knowing they still require the Billing endpoint

Connecting Forward

Phase 4 builds on these generative AI foundations by adding autonomy and multi-step reasoning. The chat completions API becomes the foundation for agent reasoning; the tool-calling patterns you saw here expand into code interpreters and function execution.

Self-Check Questions

1. A company wants their chatbot to always respond with factual information about their products, which change frequently. Should they use fine-tuning, RAG, or prompt engineering? Why?

2. An application generates creative marketing copy that occasionally produces repetitive phrases. Which parameters would you adjust, and in which direction?