Adding AI Features to Your App: What to Consider Before You Build

Adding an AI assistant, smart search or content generator to an app is easier than ever. A few API calls can produce impressive demos. The hard part is turning a demo into a feature that is reliable, affordable, safe and genuinely useful to your users. Many teams discover this the hard way, after launch.

This guide covers the practical questions to answer before and during your build, so your AI feature earns its place in the product.

Start with the problem, not the technology

“Add AI” is not a product goal. Start with a user problem and ask whether AI is the right tool.

Good candidates for AI features tend to share traits:

• The task is language-heavy or pattern-heavy: summarising, drafting, classifying, searching, extracting information.
• A good-enough answer is valuable, and a human can easily review or correct it.
• The cost of an occasional mistake is low or manageable.
• Users currently spend significant time on the task.

Examples include answering common support questions, summarising long threads, suggesting replies, tagging content, translating text, or helping people find what they need with natural-language search.

Poor candidates are tasks that demand guaranteed accuracy or carry serious consequences when wrong, such as medical, legal or financial decisions made without human oversight. For those, AI may assist a qualified person but should not act alone.

A useful test: if a simpler solution, like a better filter, clearer navigation or a rules-based flow, solves the problem, build that first.

Choose your approach

You generally have three paths:

1. Use a hosted model through an API. The fastest route. You send input and receive output, and the provider runs the model. Trade-offs include per-use costs, dependence on the provider and sending data to a third party.
2. Use an open model that you host yourself. More control over data and cost at scale, but more engineering and infrastructure work.
3. Use on-device models for small, privacy-sensitive or offline tasks. Models must be small, and capabilities are more limited.

For most teams starting out, a hosted API is the practical option. Design your app so the model provider is swappable, by wrapping calls in your own service layer, because models and pricing change quickly.

The big design challenges

Accuracy and “hallucinations”

Language models can produce fluent text that is wrong. They may invent facts, sources or details. Reduce the risk by:

• Grounding answers in your own data, a technique often called retrieval-augmented generation (RAG), where the system fetches relevant documents and gives them to the model as reference material
• Instructing the model to say when it does not know and to answer only from provided information
• Showing sources so users can verify
• Adding human review for high-stakes outputs
• Testing with a fixed set of real examples before every change, and tracking the results over time

Never present AI output as guaranteed truth, and make it easy for users to give feedback on bad answers.

Latency

Users notice delays. Model calls can take seconds. Make the experience feel responsive:

• Stream responses so text appears as it is generated
• Show clear loading states and let users cancel
• Use smaller, faster models for simple tasks and reserve larger ones for complex requests
• Cache common answers where appropriate
• Move non-urgent work to the background

Cost

Most hosted models charge based on the amount of text processed, often measured in tokens, for both input and output. Costs scale with usage, so model them early.

• Estimate cost per interaction and multiply by realistic usage
• Keep prompts concise and limit the context you send
• Set rate limits and usage caps, per user and overall
• Add budget alerts so a bug or abuse does not create a surprise bill
• Decide how the feature fits your business model: free, limited, or part of a paid plan

Privacy and data protection

When you send user content to a third-party model, you are sharing personal data with another party.

• Send only what is necessary. Remove or mask sensitive details where possible
• Read the provider’s data usage and retention terms. Check whether your data may be used for training, and choose settings and plans that match your commitments
• Update your privacy policy and app store privacy disclosures to reflect AI processing
• Consider regional requirements such as GDPR, including lawful basis and user rights
• Never put secrets or private keys in prompts

Safety and guardrails

AI features need boundaries.

• Prompt injection: malicious or accidental instructions hidden in user input or retrieved content can manipulate the model. Treat model input as untrusted, limit what the model can do, and never give it unchecked access to sensitive actions or data.
• Harmful or inappropriate output: use content filters, clear system instructions, and testing for edge cases. For social features, combine with the practices in our guide to trust and safety for social apps.
• Permissions: if the AI can take actions, such as sending messages or changing data, require confirmation for anything consequential.
• Transparency: tell users when they are interacting with AI and what it can and cannot do.
• Escape hatches: offer a path to a human or a manual alternative.

Also follow platform rules. App stores have policies for AI-generated content, which in some cases require ways to report offensive content, so review the current guidelines for iOS and Android before launch.

Design the experience carefully

A good AI feature feels like a helpful tool, not a gimmick.

• Set expectations with example prompts and short descriptions of what it can do
• Provide editable outputs. Users should be able to change, regenerate or discard suggestions
• Keep the human in control, especially for actions
• Make errors recoverable, with clear messages when the model fails or is unavailable
• Avoid hiding core functionality behind a chat box. Often, buttons and structured inputs are faster than typing

Measure whether it works

Define success before launch.

• Task success: did users accomplish what they wanted?
• Quality ratings: thumbs up or down, with optional comments
• Adoption and retention: do people come back to the feature?
• Cost per successful outcome, not just cost per request
• Failure rates, timeouts and escalations to humans
• Safety incidents and reports

Review real (appropriately anonymised and consented) conversations to find patterns. Update prompts, data sources and flows based on what you learn.

A cautious rollout plan

1. Prototype the core interaction with a handful of real examples
2. Test internally with a written evaluation set
3. Release to a small group, behind a feature flag
4. Monitor quality, cost, latency and safety daily
5. Iterate, then expand gradually
6. Keep a kill switch to disable the feature quickly if something goes wrong

AI feature checklist

• Clear user problem; AI justified over simpler alternatives
• Approach chosen (hosted, self-hosted or on-device) with a swappable provider layer
• Answers grounded in trusted data where accuracy matters
• Evaluation set of real examples tested before each change
• Streaming, loading states and cancel option
• Cost per interaction modelled; rate limits and budget alerts set
• Data minimisation; provider data terms reviewed
• Privacy policy and store disclosures updated
• Defences against prompt injection; confirmations for consequential actions
• Users told they are interacting with AI; human fallback available
• Store policies for AI features reviewed
• Metrics, monitoring and a kill switch in place

Final thoughts

The most successful AI features solve a specific problem, set honest expectations and keep humans in control. Start small, measure carefully and expand only when quality, cost and safety are under control. If you are exploring AI for your product and want help scoping a sensible first version, see our app development service or contact us. For wider context on shipping a first version, read our guide to planning an MVP.