The outcome

Release a bounded language feature with measured behavior, explicit data handling, production controls, and a reversible rollout.

Step by step

A workflow you can repeat.

  1. 01

    Define users, task, input classes, prohibited data, expected output, quality and safety metrics, context distribution, latency, throughput, budget, retention, and rollback thresholds.

  2. 02

    Create a dedicated server-side key, record the AI21 Studio or partner deployment, exact Jamba model, endpoint, context and output limits, rate limits, terms, region, and failure contract.

  3. 03

    Build a frozen evaluation set with normal, edge, multilingual, long-context, no-answer, conflicting, unsafe, malformed, injection, timeout, and provider-error cases plus review rubrics.

  4. 04

    Run versioned prompts with explicit roles, output bounds and schemas, conservative retries and timeouts, and redacted telemetry; validate every parsed field and separately moderate consequential use.

  5. 05

    Gate on quality, safety, tail latency, errors and task cost, canary a small audience, monitor drift and spend, and retain the prior model, prompt, endpoint, and application behavior for rollback.

Working standard

What good use looks like.

  • Pin the complete model configuration.
  • Evaluate long-context failures, not just averages.
  • Canary with a tested fallback.

Official references

Check the current product documentation.