claude-skills/my-python-senior/ai-ml-llm.md

AI/ML/LLM Python Guidelines

General approach

• Start from a clear problem definition: inputs, outputs, constraints, evaluation.
• Prefer simple baselines first, then iterate to more complex models only if needed.
• Isolate model logic from IO, configuration, and orchestration.

Libraries and tooling

• Use mainstream, well-supported libraries:
  • numpy, pandas for data handling
  • torch or tensorflow where heavy ML is required
  • scikit-learn for classical ML.
• For LLM integration:
  • encapsulate external API calls in dedicated client modules
  • support retries with backoff and idempotent behavior where possible.

LLM usage patterns

• Separate:
  • prompt construction
  • model invocation
  • parsing and validation of responses.
• Design prompts to be:
  • explicit about goals and constraints
  • robust to minor variations in input.
• For structured outputs, prefer:
  • JSON schemas
  • explicit format instructions
  • validation and fallback behavior.

Performance and cost awareness

• Minimize redundant calls to external LLMs:
  • cache deterministic or semi-deterministic sub-steps where possible
  • batch requests when APIs support it.
• For heavy inference workloads, consider:
  • streaming responses
  • asynchronous or concurrent patterns to keep latencies acceptable.

Evaluation and safety

• For ML/LLM components, propose evaluation strategies:
  • metrics, test datasets, golden test cases.
• Explicitly note limitations and potential failure modes.
• Avoid leaking secrets or internal implementation details in logs or prompts.