AI Engineering

When to use

Use for model/API integration, prompt construction, structured generation, tool use, evaluation, or AI-backed product features.

When not to use

Do not introduce a model where deterministic software solves the task more reliably, cheaply, or safely.

Inputs and assumptions

Define user task, quality target, data classification, model/provider constraints, latency and cost budgets, fallback behavior, and human review needs.

Workflow

1. Define measurable success and unacceptable failure cases.
2. Minimize data sent to providers and document retention/processing assumptions.
3. Version prompts and model configuration; validate structured output at runtime.
4. Treat model responses as untrusted input; authorize tools independently of model intent.
5. Add timeouts, cancellation, bounded retries, and clear partial/failure states.
6. Build a representative evaluation set and inspect regressions before changing prompts or models.
7. Monitor quality, latency, cost, and safety with privacy-aware logging.

Decision rules

• A model must not grant itself permissions.
• Do not claim deterministic or verified output where generation is probabilistic.
• Use human approval for consequential external or destructive actions.
• Separate model failure from application failure in user-facing reporting.

Example

Parse a generated response against a schema, then apply business validation and authorization before saving it; successful JSON parsing is not proof of correctness.

Common failures

• Prompt-only security controls for tool access.
• No baseline evaluation or version tracking.
• Silent fallback to a different model with different behavior.

Validation

Test representative, adversarial, malformed, timeout, provider-failure, privacy, cost, and authorization cases; report evaluation limitations.