AI Implementation Maturity Scorecard

Have you moved beyond the Claude.ai interface to using the API directly?

The API unlocks programmatic access, custom system prompts, and production deployments.

Do you have a written system prompt in version control?

Treating your system prompt like code — with commits and review — is a hallmark of a mature implementation.

Do you have evals running to catch regressions when you change your prompts?

Evals are the AI equivalent of unit tests. Without them, you find out about regressions from users.

Do you have structured error handling for API failures and model refusals?

Production apps need fallbacks, retry logic, and user-facing error messages that make sense.

Are you actively monitoring your token usage and API costs?

Cost surprises are common. Logging tokens per request from the start makes optimization possible later.

Are you using prompt caching for repeated context (system prompts, documents)?

Prompt caching can cut costs by 60–90% on context that repeats across messages.

Do you have a data handling policy for what goes into Claude's context window?

Personal data, confidential documents, and PII all raise compliance questions. Know what you're sending.

Do you have rate limiting in place on your Claude API endpoints?

Without rate limits, a single aggressive user or bot can exhaust your API budget.

Do you store conversation history in a database (not just in memory)?

In-memory chat state disappears on refresh. Persistent history enables context-aware conversations.

Have you designed for the case where Claude's output is wrong or harmful?

Human review checkpoints, confidence thresholds, and graceful degradation paths — not just happy path.