AWS Certified Cloud Practitioner

Explanation:

Few-shot prompting with 3–4 high-quality API examples from the microservice is the most effective approach because:

1. Contextual Learning: By providing specific examples from the actual microservice, the model learns the exact patterns, conventions, and structures used in that particular codebase.

2. Pattern Recognition: LLMs excel at recognizing and replicating patterns. High-quality examples give the model clear templates to follow.

3. Consistency: This approach ensures the generated code maintains consistency with existing code patterns, naming conventions, and architectural decisions.

4. Accuracy: Compared to other methods:

   • Chain-of-thought prompting is better for reasoning problems, not necessarily code generation

   • Role-based prompting sets context but doesn't provide concrete examples

   • Temperature = 0 with no examples produces deterministic but potentially generic output

5. Practical Application: In real-world scenarios, providing examples is the most reliable way to get accurate, production-ready code that matches existing patterns.