At a glance
What you need to know.
A generative AI feature needs a bounded task, permitted information and a way to judge its output. Build the model interaction inside an application contract that can validate results, enforce authority and route uncertainty to a person rather than treating fluent text as proof of correctness.
Choose a task with reviewable output
Describe the feature in operational terms: prepare a summary of an approved document, suggest a support classification or answer a question using permitted internal sources. Establish what a useful result contains and what the feature must refuse or escalate. Compare the proposed approach with search, templates or deterministic rules. A general conversation interface can conceal many different tasks with different risks and evaluation needs; select one bounded task for the first release. Identify who reviews mistakes and how the existing process continues when generation is unavailable. The design should improve a concrete workflow rather than depend on an undefined expectation that a model understands everything.
Construct context from approved, authorized sources
Define source ownership, permitted users, document versions and freshness expectations. Retrieve only information the current user may access and preserve references that allow a reviewer to inspect the original material. An index containing a document does not establish that every user may read it. Apply the same boundary to summaries, cached answers and exported results. Review provider data handling and deployment constraints before transferring private records. Keep credentials out of prompts and use representative test material during development. If the available sources do not support an answer, the application should express that limitation instead of asking the model to fill the gap convincingly.
Separate content from application authority
Documents, web pages and user messages can contain text that tries to redirect model behavior. Treat that content as untrusted input and ensure it cannot expand permissions or authorize a tool operation. Prompt wording can help communicate boundaries, but the application must enforce them independently. Expose only necessary tools and validate the requested operation, arguments and record ownership before execution. Require human approval for actions whose scope demands it, showing the proposed change and relevant source evidence. Keep generated code or commands out of automatic execution unless a separately reviewed environment and task explicitly establish that capability and its containment.
Validate output before it reaches a business record
Use an output contract appropriate to the task, such as a bounded classification, structured draft or source-linked answer. Validate expected fields and allowed values, and reject unsupported instructions or unexpected content. A syntactically valid response can still be factually wrong, so check task-specific meaning where possible and preserve reviewer judgment for uncertain results. Keep a draft separate from an approved record. Track model, prompt and retrieval versions with the result so a correction can be investigated. Plan for incomplete output, provider errors and repeated requests; generating a second draft should not silently create a second committed business action.
Evaluate ordinary, ambiguous and adversarial inputs
Create representative cases with expected decisions or a review rubric approved by the task owner. Include missing evidence, contradictory sources, unfamiliar formats and content that asks the model to ignore the application's rules. Compare answer support, refusal behavior, classification errors and unnecessary escalation alongside latency and cost under stated assumptions. Test the actual retrieval and permission path, not just the model in isolation. Preserve a held-out review set when changing prompts or models. Record limitations and failure categories in the release decision. A polished answer on selected examples is insufficient evidence for a feature that must handle varied inputs safely and usefully.
Operate with versions, fallback and correction ownership
Introduce the feature to a bounded audience with clear draft or review states and a usable manual path. Keep model configuration, prompts, source indexing and validation in a controlled release process. Define a pause condition for unsupported answers, access failures or a provider outage and prove that the primary application remains usable. Monitor corrections, fallback frequency and task completion without logging private source text indiscriminately. Assign owners for source updates, evaluation and response to incidents. Acceptance should include integrated role testing, reproducible evaluation, validated outputs and the demonstrated pause path. Availability, model choice and operating coverage are confirmed separately for the scoped engagement.
Before you start.
- Select a bounded task, refusal rules and a review owner.
- Use authorized sources with version and reference information.
- Enforce application permissions independently of prompt content.
- Validate generated fields and keep drafts separate from approval.
- Evaluate missing evidence, contradictory sources and injection attempts.
- Release controlled versions with correction, pause and fallback paths.
Common questions.
Does retrieval prevent unsupported answers?
Retrieval can provide relevant context and source references, but the model can still misinterpret or add information. Evaluate the complete retrieval and generation path. Use source-aware review, task-specific validation and an unknown-answer response when the approved context cannot support the request.
Can prompt instructions enforce permissions?
They can describe desired behavior, but authorization must be enforced by the application. Validate user authority, record ownership and tool arguments outside the model. Treat retrieved content as data and use explicit review for actions that require approval.