Applied AI / 4 min read
Useful AI starts with a review step
A practical framework for deciding what to automate, what to validate and where to keep a human in the loop.
Start with a bounded job
Choose one input and one useful output: a support conversation turned into a draft response, a document turned into structured fields, or a product brief turned into test cases. Write down what a good result looks like before choosing a model.
A useful first workflow has an owner, a clear stopping point and a way to reject an answer. If success means ‘make our company more efficient’, narrow the task until someone can judge the output in a few minutes.
Separate generation from action
Treat a generated response as a proposal. Check its structure and required fields with ordinary code. Check important factual claims against the original source. Put externally visible actions, such as publishing a page or sending a customer message, behind a review step until the workflow has earned that responsibility.
Consider a support assistant: retrieving an order and drafting an answer are different permissions from issuing a refund. Give each step only the access it needs. A fluent answer is not evidence that an action is appropriate.
Keep a small evaluation set
Collect representative examples: an ordinary request, missing context, conflicting instructions, malformed input and an answer that should be declined. Record the expected behavior and the reason. Include cases from actual failures as the workflow runs.
Compare changes against the same examples. A faster model or a shorter prompt is useful only if the quality of the completed task remains acceptable. Keep the prompt and model configuration with each run so a regression can be investigated.
Design the failure path
Define timeouts, retry limits and spending limits. A rejected response should return to a queue or a person with its original context. Avoid retrying a whole workflow when an earlier step has already sent a message or changed a record.
For delayed HTTP callbacks, a scheduler can handle delivery timing and attempts. It cannot decide whether a generated answer is correct. Keep content validation and delivery reliability as separate responsibilities.
Measure completed work
Track accepted outputs, correction time, end-to-end latency and cost per completed task. Compare these with the original manual process. Counting generated words or workflow executions can hide a system that creates more review work than it removes.
Expand autonomy only when the evidence supports it. Start with reversible internal tasks, keep a manual fallback and revisit the decision when inputs or model behavior change.