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Yuri QueirozDesenvolvedor full-stack

Guide / applied AI

An AI feature becomes useful when its failure is designed too.

A practical framework for sources, tools, evaluation, handoff and monitoring in operational AI systems.

01

Start with a bounded job

Choose a task with an observable input and outcome: classify a request, retrieve an approved policy, prepare a draft or call a controlled tool. Broad instructions such as 'answer anything about the company' make evaluation impossible and push hidden policy decisions into a model prompt.

02

Control knowledge and action separately

Retrieval should show which source supported an answer and what happens when evidence is missing. Tool access requires its own authorization, validation and idempotency. A fluent model response must never be treated as proof that an external action succeeded.

03

Evaluate representative failure

Build a set of normal, ambiguous, adversarial and incomplete conversations. Measure task completion, unsupported claims, safe refusal, handoff quality, latency and cost. Re-run evaluation when models, prompts, sources or tools change.

04

Operate with a human path

Users need a clear route to a person when confidence or authorization is insufficient. Preserve relevant context during handoff without exposing unnecessary data. Logs, monitoring and a kill switch are operational requirements, not polish added after launch.

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