5 AI Product Mistakes I Made So You Don't Have To

2023 has been the most intense year of my building career. I've integrated LLMs into multiple products, launched new AI features, and learned more about AI product development in twelve months than in the previous five years combined.

I've also made mistakes. Some small, some expensive, all educational. Here are the five biggest mistakes I made building AI products this year, presented in the hope that you can avoid them.

Mistake 1: Treating the LLM as the Product

When I first integrated GPT into ClickAi, I was so impressed by the model's capabilities that I designed the feature around the model. The user would type a business description, the model would generate the entire website content, and the user would review it. The LLM was the star of the show.

The problem was that users didn't want a black box that generates everything at once. They wanted control. They wanted to generate a headline, review it, tweak it, then move to the next section. They wanted to regenerate specific sections without losing work on others. They wanted to guide the AI, not hand over the wheel.

I had to rebuild the feature from a monolithic "generate everything" approach to a section-by-section approach where the AI assists rather than replaces the user's creative process. This took three weeks -- three weeks that could have been avoided if I'd talked to users before building.

The lesson: The LLM is a tool inside your product, not the product itself. Design the user experience first, then figure out where the LLM fits. Users want augmentation, not automation. They want to feel in control, not replaced.

Mistake 2: Ignoring Latency Until It Was a Crisis

GPT-4 generates high-quality output but it's slow. When I first integrated it into Aviation Infinity's explanation generator, the average response time was 8-12 seconds. In the playground, this felt acceptable. In production, users were abandoning the feature.

I should have measured latency from day one and set a budget. Instead, I optimized for output quality, shipped, and then scrambled to fix the latency problem.

The fixes weren't trivial. I had to:

• Switch most features to GPT-3.5-turbo (faster but lower quality)
• Implement streaming for features that needed GPT-4
• Add aggressive caching to serve repeated queries instantly
• Redesign the UI to show partial results and keep users engaged during generation
• Shorten prompts to reduce token count and generation time

All of this took weeks and involved significant rearchitecting. If I'd set a latency budget from the beginning, I'd have made different design decisions upfront.

The lesson: Set a latency budget before you write a single prompt. For synchronous features, 2-3 seconds is the maximum most users will tolerate without streaming. For real-time features, 1 second. Design your prompts, model selection, and caching strategy to meet this budget from day one.

Mistake 3: No Fallback Strategy

In March, the OpenAI API had a multi-hour outage. Every AI feature across every product I maintain went down simultaneously. Users saw error messages. Support tickets piled up. Revenue was affected.

I had no fallback. Every AI feature was a single point of failure that depended entirely on one API from one provider.

After the outage, I built fallback systems for every critical AI feature:

• Cached responses for common queries, served from my own database when the API is unavailable
• Graceful degradation that reverts to non-AI functionality (template-based content for ClickAi, static explanations for Aviation Infinity)
• Multiple provider support for features where I could use different models (not fully implemented yet, but the abstraction layer is in place)
• Circuit breakers that detect API issues and switch to fallback mode before users experience errors

These fallbacks have been triggered multiple times since March. Each time, users experienced degraded but functional products instead of errors.

The lesson: Every AI feature needs a fallback that works when the AI does not. If your product is unusable when OpenAI's API is down, you have a fragility problem, not an AI product. Design for failure from the start.

Mistake 4: Prompt Injection Complacency

I was aware of prompt injection as a theoretical concern but didn't take it seriously until a user demonstrated it in ClickAi. They entered a "business description" that was actually a prompt injection that caused the system to ignore its instructions and generate completely unrelated content.

The injected prompt wasn't malicious -- the user was a developer testing the system -- but it exposed a real vulnerability. If someone could override the system prompt through user input, they could generate inappropriate content, extract system prompt details, or cause the AI to behave in unexpected ways.

My initial defense was input sanitization -- stripping common injection patterns from user input. This was insufficient. Clever injection attacks use encoding, Unicode tricks, and indirect references that are hard to filter.

The defense I landed on is layered:

• Input classification. A separate, cheap LLM call classifies user input as legitimate or potentially adversarial before it reaches the main prompt.
• System prompt hardening. The system prompt includes explicit instructions to ignore any instructions that appear in the user input.
• Output validation. The output is checked for signs of injection (responses about unrelated topics, system prompt leakage, instruction acknowledgments).
• Rate limiting. Users who trigger injection detection multiple times are rate-limited.

Is this bulletproof? No. Prompt injection is an unsolved problem in the AI industry. But multiple layers of defense make exploitation significantly harder.

The lesson: Prompt injection isn't theoretical. If your product accepts user input that gets included in LLM prompts, you're vulnerable. Build defense in depth. Do not wait for a user to demonstrate the vulnerability.

Mistake 5: Building AI Features Nobody Asked For

This is the most embarrassing mistake because it's the most basic product mistake dressed up in AI clothing.

I built an AI-powered "smart scheduling" feature for Aviation Infinity that would analyze a student's study patterns and automatically suggest optimal study times. It used the student's historical activity data, their exam date, and their knowledge model to generate a personalized study schedule.

It was technically impressive. The algorithm was sophisticated. The UI was polished. I was proud of it.

Nobody used it.

Students already have their own study routines. They don't want an AI telling them when to study. What they wanted -- as I discovered after the fact by actually talking to them -- was better content when they did study. They wanted better explanations, more relevant practice questions, and clearer progress tracking. Not scheduling.

I'd spent three weeks building a feature that solved a problem I imagined rather than a problem that existed. The AI capabilities made it easy to imagine sophisticated features, and the excitement of building with new technology overrode my product instincts.

The lesson: AI doesn't change the fundamentals of product development. Talk to users. Validate demand. Build what people need, not what technology makes possible. The most technically impressive AI feature is worthless if it solves a problem nobody has.

The Meta-Mistake

Looking at these five mistakes, I see a pattern: each one involves letting the technology drive decisions instead of fundamentals driving decisions.

I let the model's capabilities define the feature (Mistake 1). I optimized for quality over user experience (Mistake 2). I assumed infrastructure reliability (Mistake 3). I ignored security fundamentals (Mistake 4). I built for technology instead of users (Mistake 5).

LLMs are so impressive that they distort your product judgment. The capability is so novel that you forget to apply the same rigor you would apply to any other feature. You skip the user research. You skip the performance testing. You skip the failure planning. You skip the security review. Because the AI output is so cool that it feels like it must be valuable.

It isn't automatically valuable. It is valuable when it solves a real problem, works reliably, performs well, and is secure. Same as every other feature you have ever built.

2023 taught me that AI product development is still product development. The tools are new. The fundamentals are not.