Why Reliability Will Make or Break Your AI Product
I've been thinking about Reliability and AI for a while. After over a year of developing and iterating on AI applications, I am convinced that reliability can make or break your applications.
AI applications have gone from prototypes to core elements of software engineering in the past few years, such as recommendations, identifying fraud, help with writing, and a lot more. However, with great power comes great unpredictability. Reliability is no longer just a feature; it is the foundation. Users may be impressed by intelligence, but they return for consistency. If your AI system fails unexpectedly, even once, users' trust in it will erode faster than it was built. And once that trust is lost, it's nearly impossible to regain.
What makes issues even harder is that AI does not always fail in obvious ways. There may be no error message or crash, just an off-putting response, a recommendation that falls short, or a moment when a user feels unseen. These are the times when reliability becomes obvious. They determine whether your product succeeds or fails.
So, reliability is no longer just a backend concern; it now extends to the way we build products. It doesn't always appear on launch day. However, it is what encourages a user to return tomorrow. And for the AI systems we're developing, I can't think of a more important metric.
What Makes Reliability in AI Different
Traditional software is largely deterministic: the same input produces the same result every time. AI is probabilistic. Even with identical inputs, we may receive different results depending on model randomness, inference parameters, hyperparameters, or changes in the upstream data. This makes bugs harder to reproduce and problems more difficult to detect.
Furthermore, AI systems degrade over time due to data changes rather than code changes. Models trained on yesterday's data may not be accurate tomorrow. This is known as data drift or model decay, and it is one of the most significant reliability risks in production machine learning.
LLM reliability extends beyond uptime and performance. It is about the predictability of behavior. It's about how the system handles uncertainty.
Common Ways LLM Fails
Hallucinated responses: The AI makes things up but expresses them with such confidence that users don’t realize anything went wrong.
Inconsistent behavior: Asking the same question twice or multiple users going through the same flow generates different results, creating confusion.
Lack of graceful handling for edge cases: The system doesn’t know how to say “I don’t know” and instead offers low-quality or misleading responses.
Data Drift: As the training data becomes outdated, models begin to fail in new, unanticipated ways.
Ways that have helped me improve Reliability
Don’t just test functionality but also behavior: Traditional test cases validate “Does this work?” But with LLMs, I’ve learned to ask, “How will this feel?” “What if this output is wrong but sounds right?”
Observe real users and not just metrics: Quantitative metrics are helpful, but they often miss the story, and hence qualitative metrics play an equally important role. Watching and reading the chat histories of users’ interactions with our LLM features has been far more enlightening. You start to notice patterns, confusion points, or expectations that weren’t met.
Build with confidence in mind: For every LLM response, we ask, “How confident are we in this output?” If the answer is “not very,” then the UI should reflect that, and we iterate further on how to increase the confidence in the LLM response.
Avoid frustrating loops: Users should never feel trapped inside an LLM-generated flow. There should always be a way to correct, back out, or ask for help. Even if there is a failure in response, there should be an action item/next step for the user to break the loop.
Remember the worst cases: One thing that’s helped me is mentally walking through the “worst case” for any new AI feature. What happens if the model returns garbage? What does the user see if our inference endpoint is down?
Make reliability part of the definition of done: We often think a feature is complete when it works — but it’s only truly done when it works reliably. That includes graceful failure, observability, and user clarity. I now treat those as first-class aspects of every AI feature, not just add-ons.
Reliability is ultimately about Respect. Respect for the user’s time. Respect for their mental model. And respect for the trust they place in what we build.
The Role of Humans in the Loop
No matter how advanced the model, it needs human context or a human in the loop to stay grounded. That’s why human-in-the-loop (HITL) systems aren’t just a safety net; they’re a reliability engine.
Human-in-the-loop ensures:
Critical errors are detected before they reach the user.
Ambiguities are resolved through human judgment.
User feedback is fed back into the system to help it improve over time.
Even lightweight HITL systems, such as feedback buttons, add a layer of adaptability that code cannot provide.
We often talk about AI’s capabilities: its speed, scale, or intelligence. But what truly matters in the long run is its Reliability. That’s what users come back to. And that’s what determines whether an AI system quietly fades away or becomes a trusted part of a user’s journey.
Reliability doesn’t announce itself. It shows up in the quiet moments; when things just work, even when the stakes are high or the path is unclear. Reliability isn’t just a technical challenge. It’s a design principle. A product value. A reflection of how much we care.
And in the world of AI, it just might be the thing that makes or breaks everything else.