How to Integrate AI Into an Existing App

Start with a repeated task where imperfect output can be inspected or corrected. In our example, the goal is to reduce the time spent assembling weekly project summaries while preserving blockers, dates, and unresolved decisions. The baseline is the current manual workflow, measured on representative projects.

Write down what the feature may do: summarize selected updates and identify unanswered questions. Then define what it may not do: change deadlines, mark work complete, invent commitments, or publish a report. If the model is unavailable, users can still read the source updates and write their own summary. This makes the feature useful without making the entire product depend on it.

The browser submits the project identifier, selected update identifiers, and a request identifier. The backend authenticates the user, checks access to the project, loads the authorized records, and constructs the model request. Keep provider credentials out of browser code and mobile application bundles.

Never trust a client-supplied workspace identifier as proof of permission. Load records through the same authorization rules used elsewhere in the app. Sending a forbidden record to a model is already a disclosure, even if a later response filter hides it from the user.

Send the minimum context needed for the task. A status summary may need update text, authors' display names, and dates; it rarely needs billing details, private attachments, or every historical conversation. Review provider data retention, deployment region, and contractual settings against the actual data you plan to send. Do not assume every API account has the same configuration.

Define the output your interface needs before writing the prompt. For the summary feature, that could be an overview, a list of blockers with source update identifiers, and a list of unresolved questions. Allow empty values where information is absent instead of encouraging the model to fill every field.

Providers may support structured outputs constrained by a schema. This can help your application parse results consistently. It does not establish whether a deadline is accurate or a referenced record belongs to this project. Check those facts in your own code.

For example, reject a source identifier that was not in the authorized input set. Flag a reported deadline that cannot be traced to a source update. Require numeric values to pass domain rules, not just a numeric type check. Keep the distinction between “valid format” and “acceptable result” visible in your implementation and tests.

An output can be syntactically valid and still omit the most important blocker. Show the draft alongside links to the source updates so a user can review it efficiently. Label generated content as a draft, preserve edits, and make the save action explicit.

If validation fails, choose a bounded response: return a useful error, ask the model for one repair attempt, or offer the manual workflow. Avoid unlimited retry loops that quietly consume money. A repair prompt should describe the specific validation failure without adding unrelated confidential context.

Choose between a synchronous request and a background job based on typical duration, payload size, and the user's workflow. A short summary might finish while the user waits; processing a collection of long documents may need a queued job with visible status. Measure real requests before deciding.

For a background job, store a request identifier, owner, status, input version, timestamps, and result or failure reason. The interface should distinguish waiting, running, failed, and complete. Refreshing the page should reconnect to the same job rather than create a new one. Set a maximum processing duration and a clear recovery path.

Retry temporary failures selectively with a limit and delay. A retry can cause another model charge even if your app ultimately stores only one result. Use idempotency in your own job creation and save operations, and verify any provider-specific guarantees separately. Do not assume that sending an arbitrary idempotency header makes model calls free of duplication.

The AI response should not overwrite the user's current work as soon as it arrives. Store it as a candidate associated with the input version. If the underlying project updates change during generation, indicate that the draft may be outdated and let the user refresh it.

Use a version check when saving so a late AI response cannot erase a more recent human edit. In the example app, saving the summary changes only the summary record; it does not close tasks or alter due dates. Adding those actions later requires separate permission checks and explicit product decisions.

Record request duration, model and prompt version, outcome, validation errors, and usage needed for cost monitoring. Use a correlation identifier to connect a browser error to a backend job. Keep sensitive content out of routine logs; define retention and access for any samples retained for quality review.

Review cost per accepted summary, edit burden, failure rate, and time saved against the manual baseline. Acceptance alone can be misleading if users save drafts with major errors. Periodically inspect a sample with the people who understand the source material.

Use fixed test inputs and expected behaviors, not an assertion that every output must match one exact paragraph. Separate deterministic application tests from quality evaluations of model responses. The former check permissions and state transitions; the latter check completeness, grounding, and usefulness.

Start with an internal or opt-in group that understands the feature's limitations. Review failures on the same kinds of projects that will use it in production. A demo using short, clean updates does not predict behavior on months of contradictory notes.

Decide launch criteria before expanding access. These could include no unauthorized source access in the security tests, no lost edits in concurrency tests, and an agreed quality threshold on representative summaries. Choose thresholds appropriate to the task rather than borrowing a universal accuracy number.

Keep the prompt, model selection, retrieval settings, and validation logic versioned. Re-run the evaluation set when any of them changes. A provider upgrade may improve general capability while changing the exact behavior your workflow relies on.

If the feature needs information outside the selected records, add a permission-aware retrieval step or a deterministic data lookup. If failures persist because the model repeatedly misunderstands a stable specialized task, investigate better examples and possibly fine-tuning. Our RAG versus fine-tuning guide separates these failure modes.

You do not need a provider abstraction for every theoretical future model. A small boundary around request construction, response parsing, and error handling is often enough to keep your business logic independent of one SDK. Add complexity when a real requirement justifies it.

Often, yes. A narrow feature can use the existing interface, backend, authentication, and database. The main work is understanding the workflow and adding reliable boundaries around the model. If the existing app cannot enforce data access consistently, address that weakness before exposing records to a new integration.

A hosted model using a private API credential should normally be called through your backend. On-device models are another architecture with different capability, device, and distribution constraints. They do not remove the need to validate important outputs.

It can be designed that way, but start by separating generation from execution. Validate the proposed action, check the user's authority, and require confirmation where a mistake has meaningful consequences. A generated suggestion should not become permission to send, delete, purchase, or modify records.

Ask for the user flow, data boundary, output contract, failure behavior, evaluation plan, and operating-cost estimate. Discuss an AI integration with DevConex using one real workflow and a few representative examples as the starting point.