Building with AI is genuinely exciting until the first time a user screenshots your app hallucinating something ridiculous and posts it on social media.
The mistakes that hurt are not always the obvious ones. Everyone knows you should validate AI output. But there are subtler traps that show up after you have shipped, when users are doing things you did not expect.
Here are the ones that keep coming up.
Trusting the model with user trust
The biggest mistake: treating AI output as ground truth and presenting it to users as if it is.
“Our AI confirmed your appointment for Tuesday” — when the AI actually just pattern-matched on the conversation and made an assumption.
“Based on your documents, you owe X in taxes” — when the model made a calculation error and you displayed the result without validating it.
Users believe what your app tells them. If your app says the AI confirmed something, they will not double-check. The correction, when it comes, erodes trust permanently.
The fix is not difficult: never present AI output as confirmed fact without a checkpoint. Show it as a suggestion, draft, or estimate. Give users a way to verify or override. Make it clear when something came from AI and when it came from your system.
Ignoring what happens at the edges of the prompt
You test your app with normal inputs. Users are not normal.
They paste entire legal documents into a chat box sized for three sentences. They ask in languages you did not test. They try to get your customer support bot to write poetry. They copy-paste binary data into a text field.
Edge inputs break prompts in ways that are hard to predict. A prompt that works perfectly with typical inputs might produce garbage, error out, or behave unexpectedly when the input is too long, too short, in the wrong language, or off-topic.
Test your prompts with adversarial inputs before launch. Specifically:
- Empty input
- Input that is 10x longer than expected
- Input in a language you did not design for
- Input that is completely off-topic for your use case
- Input that contains HTML, JSON, or code mixed with natural language
Each of these can break a prompt in a different way. Find out in testing, not in production.
Building long context chains without reset points
Conversation history is powerful. It lets AI remember context across a session. It also lets errors compound.
If the model misunderstands something in message 3, and you keep the conversation history, messages 4 through 20 might all be building on that misunderstanding. By the time the user realizes something is wrong, you have 20 turns of context that are all anchored to the wrong premise.
This is especially bad in agentic workflows where one step’s output becomes the next step’s input. A bad intermediate result can propagate all the way to the final output.
Build explicit reset points. Let users restart a conversation. In multi-step workflows, validate intermediate outputs before using them as inputs to the next step. Do not assume that because the previous step returned something, it returned something correct.
Not handling the model going off-topic
You build a cooking assistant. A user asks it for relationship advice. What does your app do?
If your system prompt just says “you are a helpful cooking assistant”, the model might actually try to help with the relationship question — because it is helpful by default.
Now your cooking app has given someone relationship advice. That is not catastrophic, but it is not the product you intended to build. In a financial app or medical context, it could be genuinely harmful.
Test explicitly for off-topic queries. Tell the model what to do when a user asks something outside its scope. “If asked about anything other than cooking, respond with: ‘I’m a cooking assistant — I can only help with food and recipes. Is there something cooking-related I can help you with?’”
Then test it. Try to get the model to break character. If it does, tighten the prompt.
Displaying raw model output
The model returns text. You display it. Done?
Not quite. AI output can contain:
- Markdown formatting that your UI does not render (so users see
**bold**instead of bold) - JSON wrapped in prose (“Here is the data you requested: {…}”)
- Inconsistent casing, punctuation, or formatting
- Trailing whitespace or line breaks that look odd in your UI
- Unexpected length (the model decided to be very thorough today)
Post-process the output. Strip markdown if your UI does not render it. Parse JSON if you asked for JSON. Trim whitespace. Set a max length and truncate gracefully. Treat model output the same way you treat user input — do not trust that it will come back in exactly the format you expect every time.
Skipping rate limiting and cost controls
A dev builds an AI feature. They test it. Works great. They ship it. On day three, a user writes a script that hammers the endpoint 10,000 times.
Without rate limiting, you pay for 10,000 API calls. At Sonnet pricing with decent-length prompts, that could be a significant unexpected bill overnight.
Rate limit every AI endpoint. Per user, per IP, or both. Set a daily spending cap in the Anthropic console. Add server-side limits on input length so users cannot send 100,000-token prompts that eat your budget in one call.
None of this is AI-specific — it is basic API hygiene. But developers sometimes skip it for internal AI features because they trust their users. You should not.
Not logging and monitoring
When something goes wrong with a traditional API call, you have logs. When something goes wrong with an AI feature, you have… the user complaint, and no visibility into what prompt was sent, what context was in play, or what the model responded with.
Log every AI call in production: the prompt, the relevant context, and the response. Store it somewhere you can query. Not forever — just long enough to investigate problems.
Set up monitoring for things that matter: response latency, error rates, and response length distribution. A sudden spike in response length often means the model started doing something unexpected.
You cannot fix what you cannot see.
The common thread
Most of these mistakes share the same root: building with the happy path in mind and forgetting that AI systems fail in ways that are qualitatively different from code.
Code fails with errors. AI fails by being confidently wrong, by drifting off topic, by doing something almost-right that is subtly wrong, by behaving differently on inputs you did not test. These failures are harder to detect and harder to debug.
The developers who ship reliable AI features treat the model as an external system they do not control — because they do not. They validate inputs. They validate outputs. They monitor. They build escape hatches for when things go sideways.
That mindset is the difference between an AI feature that users trust and one that ends up in a screenshot.
Related: Designing AI-Native Features: What to Build vs What to Prompt · Prompts That Go Wrong: What I Learned Shipping AI Features
Related Reading.
Designing AI-Native Features: What to Build vs What to Prompt
A practical decision framework for knowing which app features should use AI, which should stay in code, and how to avoid the most common trap in AI product design.
Prompts That Go Wrong: What I Learned Shipping AI Features
Real examples of prompts that looked fine in testing and broke in production — and what I changed to fix them. A field guide for developers writing prompts for real users.
How to Add Claude to Your App Using the Anthropic API
A practical guide to integrating Claude into your app with the Anthropic SDK — from first call to streaming, context management, and common usage patterns.