# 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.