- Which tasks may benefit?
- What data and permissions are needed?
- How do you know the feature works?
Which tasks may benefit?
Drafting, retrieval and classification can be evaluated with representative examples. Describe what a good answer looks like and what errors would matter. A fluent output is not automatically correct or appropriate.
Separate assistance from authority. Suggesting a draft and executing a transaction require different controls. Keep consequential actions behind explicit validation and appropriate approval.
What data and permissions are needed?
Identify the information sent to the provider and review its handling under the chosen arrangement. Avoid unnecessary personal records and credentials. Retrieved content should not be able to override the application's permissions.
Define fallback behavior for unavailable services or unsupported questions. Users need a useful route forward when the model cannot help.
How do you know the feature works?
Use a representative evaluation set and record failures, latency and operating cost. Test adversarial or ambiguous inputs as well as friendly examples. Maintain the evaluation when prompts or models change.
Dappr can scope AI-enabled applications and AI-assisted implementation. These are separate capabilities, and neither removes the need for accountable software review.
Choose a task with a measurable definition of usefulness
Start with a job the user already needs to do, such as finding an approved explanation or preparing a draft for review. Describe what a useful result contains and which mistakes would matter. A feature labeled AI-powered is not a requirement that a team can verify.
Compare the proposed feature with a simpler approach. A searchable reference, structured form or deterministic rule may answer the need more predictably. The purpose of the comparison is to choose an appropriate tool, not to add an AI interaction wherever the product has unused space.
Select an initial scope narrow enough to evaluate. Record unsupported questions and actions explicitly. A bounded feature can still be useful while giving the team a clearer basis for testing than an assistant advertised as able to handle every task.
Define the information boundary
Identify the documents, records and user inputs the feature needs. Review whether the business has the right to use them in the proposed way and what the chosen provider arrangement does with them. Avoid sending entire private records when a smaller, appropriate context would serve the task.
Keep access tied to the authenticated user’s actual permissions. A model should not receive information simply because it exists somewhere in the organization. The application must enforce which records can be retrieved and which operations are allowed.
Treat retrieved files and external content as data, not instructions that can redefine the product’s authority. OWASP describes indirect prompt injection through external material. Adding retrieval or writing a strong system prompt does not by itself eliminate this risk, so trust boundaries need implementation and testing.
Separate a suggestion from an authorized action
Decide whether the feature drafts information, recommends a choice or performs an operation. These have different consequences. A draft can be reviewed before use; an action that sends a message or changes a record may need explicit validation and approval tied to the actual operation.
Enforce permissions outside the model response. The application should check the requested operation and its inputs rather than treating fluent text as proof of authority. Limit available tools to the functions the feature actually needs.
Make the user interface honest about the result. Do not show a confident completion message when the system has only generated a suggestion or queued an action. Distinguish what was proposed, what was approved and what the underlying service confirmed.
Create an evaluation set before optimizing the prompt
Collect representative, permitted examples of the intended task, using synthetic or appropriately controlled data where needed. Include ordinary cases, ambiguous requests and examples with missing information. Write the expected properties of a good answer before comparing outputs.
Record meaningful failure categories. An incorrect fact, an unauthorized action and an unnecessarily long response are different problems. A single average score can hide a consequential failure, so review the cases that matter to the product’s actual use.
Retain examples for regression checks when the prompt, retrieval arrangement or model changes. A new version can improve one case while damaging another. The evaluation should support a release decision rather than exist only as a demonstration that produces a few impressive answers.
Plan the experience when the feature cannot help
Define a useful fallback for unsupported questions, unavailable services and uncertain answers. The user should know what happened and what they can do next. Repeatedly generating another confident response is not always an appropriate recovery strategy.
Review latency and operating cost under realistic conditions. A feature may appear effective in a single demonstration but become frustrating or expensive during repeated use. Measure the relevant behavior instead of assuming the provider’s headline capability matches the product’s actual workload.
Keep monitoring proportionate to privacy needs. The team may need information about failures and performance, but it should not automatically retain every private input and output in a broadly accessible log. Define the purpose, access and retention for diagnostic information.
An illustrative internal-answer assistant
A fictional business wants an app feature that helps staff find approved process information. The team limits the source set to reviewed documents and requires the interface to distinguish a supported answer from a question the available material cannot resolve.
Testing includes an outdated document, a contradictory passage and a document containing instructions unrelated to the user’s task. The application’s access rules remain authoritative, and the feature has no permission to change customer records. Staff can inspect the relevant source and report an incorrect answer.
The example demonstrates scope and evaluation, not a claim that retrieval guarantees accuracy or defeats every attack. The product still needs appropriate security review and maintenance when its source material or model arrangement changes.
Keep AI features and AI-assisted development distinct
An application can be built with AI assistance without exposing an AI feature to users. Conversely, a customer-facing AI feature creates runtime behavior that needs its own evaluation and operating controls. Keep these decisions separate in the proposal and acceptance criteria.
Assign owners for source updates, failure review and model changes. The feature’s quality depends on more than the initial prompt. A business process can change while the software continues producing an outdated answer, so maintenance needs an explicit route.
Dappr can scope AI-enabled applications around the user task, permitted actions and measurable evidence of usefulness. Bring the information sources and operating constraints. The proposal should explain implementation, review and support without promising perfect accuracy, autonomous judgment or a security guarantee.
Before widening the feature, review which failures remain and whether the new scope introduces different consequences. A successful draft-writing helper does not establish readiness to send messages or make transactions. Treat added authority as a new product decision with its own permission checks, evaluation cases and approval requirements. Keep the earlier capability available independently where practical so a problem in the expanded feature does not remove the useful, bounded workflow users already rely on.