An AI feature works perfectly during development. You send a request, receive a response, test a few examples, and everything looks good.
Then you release it.
Real users start asking unexpected questions. Some responses take longer than expected. Others contain incomplete information. API requests occasionally fail, and the application doesn't always know what to do next.
Suddenly, the feature that worked so well during testing needs more attention.
This is a common challenge when building AI-powered applications. Getting an AI model to work is one thing. Making it reliable enough for everyday use is another.
So, what should developers consider before moving an AI feature into production?
1. Testing a Few Prompts Isn't Enough
During development, it's natural to test an AI feature using a handful of carefully selected questions.
For example, imagine building an AI assistant that answers questions about internal company documents. You test it with relevant questions, and it provides accurate answers.
But real users don't always ask questions in the same way.
They might use incomplete sentences, misspell words, combine multiple questions, or ask something that isn't covered by the available documents.
An application that works well with predictable inputs may behave differently when exposed to real usage.
Developers should test a wider range of scenarios, including:
- Clear and straightforward questions.
- Incomplete or ambiguous requests.
- Questions outside the available information.
- Unexpected input formats.
- Requests involving conflicting information.
The goal isn't to make AI answer every possible question. It's to understand how the application behaves when it doesn't have enough information to provide a reliable answer.
2. Don't Assume Every AI Response Is Correct
One important difference between traditional application logic and generative AI is that an AI model can produce a response that sounds convincing but contains incorrect information.
For example, an AI assistant connected to a knowledge base might provide an answer that is only partially supported by the available documents.
Displaying that answer without checking it can create problems, especially when users rely on it to make business decisions.
Developers can reduce this risk by:
- Providing relevant and reliable source information.
- Asking the model to indicate when information is unavailable.
- Validating structured responses before using them.
- Checking important information against original records.
- Showing supporting sources where appropriate.
For applications handling financial, customer, or operational information, additional validation and human approval may be necessary.
A successful API response doesn't automatically mean the information is correct.
3. Plan for API Failures and Unexpected Delays
An AI-powered application depends on more than the model itself.
It also depends on API availability, network connectivity, request limits, processing time, and the way the application handles errors.
Imagine an employee using an AI assistant to summarise a customer interaction. The request takes longer than expected, or the API temporarily becomes unavailable.
What does the employee see?
Does the application wait indefinitely? Does it display a useful error? Can the employee retry the request without accidentally creating duplicate operations?
Developers should consider practical safeguards such as:
- Setting appropriate request timeouts.
- Handling API errors gracefully.
- Implementing controlled retry mechanisms.
- Respecting API rate limits.
- Providing useful feedback when a request fails.
For longer-running tasks, background processing may be more appropriate than keeping the user waiting for a response.
These details might not be noticeable during a basic demonstration, but they become important when real users depend on the feature.
4. Think About Security Before Connecting Business Data
AI applications often need access to documents, customer information, internal systems, or other business data.
This introduces security considerations that developers should address early.
For example, an AI assistant connected to an ERP system might retrieve customer records to answer employee questions.
The application must ensure that users only access information they are authorised to see.
Simply telling the AI model not to reveal confidential information is not sufficient. Access permissions should be enforced by the application and its data retrieval layer.
Developers should also consider:
- What information is sent to the AI provider.
- Whether sensitive data needs to be excluded or masked.
- How API credentials are stored.
- Who can access conversation history.
- How long prompts, responses, and logs are retained.
Security should be part of the application design, not something added just before deployment.
5. Monitor What Happens After Deployment
Once an AI feature goes live, development doesn't stop.
The way people use an application can change over time. New questions appear, business data changes, and model or prompt updates can affect response quality.
Monitoring only whether the API is responding successfully won't tell developers whether the feature is actually working well.
It is useful to track:
- API response times and failure rates.
- Token consumption and usage costs.
- Validation failures.
- User feedback and correction patterns.
- Whether intended application operations are completed successfully.
These observations help developers identify problems and decide where improvements are needed.
For example, if users frequently correct answers from a knowledge assistant, the problem might not be the model. The application may be retrieving outdated or irrelevant documents.
Monitoring is also part of the broader AI implementation process. From selecting suitable use cases and developing custom AI applications to integrating them with existing business systems, each stage needs consideration before and after deployment. You can explore these areas through BizzAppDev's AI implementation and engineering services.
Final Thoughts
Building an AI feature that works in a development environment is a good starting point. But production applications need to handle much more than successful model responses.
They need to manage unexpected inputs, validate information, handle failures, protect business data, and provide a consistent experience for users.
For developers, the challenge isn't simply connecting an AI API and getting a response. It's building the application around that connection so it continues to work reliably under real-world conditions.
A successful AI prototype proves that something can work. A reliable production application proves that people can depend on it.
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