3 Mistakes I Made in Building AI Applications That You Should Avoid

Through my journey at Google Gemini and Archie AI, I encountered several pitfalls that taught me valuable lessons. Sharing some mistakes I made that you can avoid in your AI project:

Insufficient Testing and Quality Assurance

The Mistake: At the start, I didn't prioritize thorough testing. I only kept a basic log of prompts along with application logs without deeply evaluating the AI's responses.

What I Learned:

Use the Right Tools: Start by keeping detailed logs of prompts and model interactions. Tools such as Helicone or Langsmith are helpful for tracking these interactions. You can also use existing solutions like loguru, Betterstack etc just make sure to structure and capture rich logs around model interactions.
Regularly Check Output Quality: Make it a habit to regularly review the AI's responses. This helps ensure the AI is performing as expected and maintains high quality.
Version Control for Prompts: Keep a clear record of all prompt versions. This makes it easier to manage model updates and understand how changes affect the performance.

Advice: Prompt and model interactions are the most critical component of your product experience. Pay sufficient attention to it.

Focusing Too Much on Cost Management

The Mistake: I became overly focused on managing costs from day one. Unlike traditional software development, AI projects often have significant operational expenses (OPEX) right from the start.

What I Learned:

Balance Cost with Functionality: While it's important to keep an eye on costs, don't let it slow down your progress. Most cloud providers offer credits to help you get started, which can ease initial expenses.
Follow the Golden Rule of Software Engineering: Make it work. Then, make it right. And then only, make it fast or cost-optimize (unless cost is your key competitive advantage)

Advice: Prioritize building a solid, functional AI application first. Cost optimization can come later once you have validated your MVP.

Not Thinking Enough About Success Metrics

The Mistake: I set overly aggressive goals without clearly defining what success looks like. This led to frustration when immediate results didn't meet expectations.

What I Learned:

Set Realistic Goals: AI technology is still evolving. It takes time to build and perfect AI experiences, so be patient and avoid setting unattainable goals.
Define Clear Metrics: Establish metrics that show you're making progress. These should measure the actual benefits your app provides to users.
Focus on Customer Benefits: For example, if your AI tool is designed to improve performance reviews, track metrics like the time users spend on PR reviews. Even if users aren't paying yet, reducing their effort is a clear sign of progress.

Advice: Define success metrics that reflect the real value your AI application offers to users and helps track meaningful progress.

Happy shipping!