An AI project is ready to build when the team can explain what success looks like, which data it may use and who will handle the output when it is wrong. Choosing a model is part of the implementation. These earlier decisions determine whether the implementation has a workable destination.
Write a bounded use case
Replace “an intelligent assistant” with a specific task: draft a response from approved knowledge, classify an incoming request or extract named fields from a document. State what the system must not do, including decisions or actions that require a person.
Identify the user, the workflow and the consequence of an error. A draft that a specialist edits needs different controls from a customer-facing answer or a change to a production record. Our guide to practical LLM use cases explores those boundaries.
Check the data before the model
Inventory the documents, databases and records required. Confirm that they are usable for this purpose, current enough for the task and accessible under the right permissions. Assign an owner who can correct outdated source material.
Do not assume that putting documents into a search index preserves their access rules. The application needs to enforce permissions at retrieval and output, with tests that demonstrate users cannot receive material outside their entitlements. Sensitive information also needs deliberate retention and provider-handling decisions.
Prepare evaluation cases
Create representative examples with reviewed expectations before tuning prompts. Include ordinary requests, ambiguous inputs, missing information, contradictory sources and requests outside the permitted scope. Keep some examples separate from development so they can show whether changes generalise.
Measure the dimensions that matter to the task. For extraction, that may be field correctness and exception rates. For knowledge search, it may include retrieval relevance, supported answers and permission enforcement. For routing, inspect important categories separately; one overall accuracy figure can hide a costly failure.
A readiness checklist
| Question | Ready evidence |
|---|---|
| What is the task? | A bounded input, output and excluded behaviour |
| May this data be used? | Documented access, purpose and handling requirements |
| How will we evaluate it? | Reviewed examples and agreed acceptance criteria |
| What happens on uncertainty? | A reviewer, fallback and visible exception queue |
| Who operates it? | Named owners for content, software and incidents |
| Is it worth running? | Expected workload and a cost-versus-value model |
Design review that people can perform
A review screen should show the proposed answer alongside the evidence needed to check it. Make corrections easy to record and route difficult cases to someone with the right expertise. A mandatory approval button is not enough if reviewers cannot tell whether the result is correct.
Record useful evaluation signals while minimising sensitive content. Decide what is retained, who can see it and how corrections become future test cases. Feedback is valuable only when it is connected to a defined improvement process.
Plan integration and ongoing cost
Consider rate limits, provider outages, retries, duplicate actions and cancellation. Keep a deterministic boundary around important operations. A model may propose an action, but the application should validate permission and business rules before executing it.
Estimate cost using expected production volume and review effort, including retrieval, storage and monitoring. Test representative latency rather than relying on a clean demonstration. Assign responsibility for prompt versions, model changes and source updates.
What a good first milestone delivers
A bounded pilot should produce evaluation results, a working exception path, integration evidence and a recommendation to expand, improve or stop. Those outputs are useful even if the initial model choice changes.
ANAWAZ provides AI engineering and workflow integration support. Bring the use case and sample workflow you want to assess.