𝗪𝗵𝘆 𝗠𝘆 𝗔𝗜 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗙𝗮𝗶𝗹𝗲𝗱 𝗔𝗻𝗱 𝗛𝗼𝘄 𝗜 𝗙𝗶𝘅𝗲𝗱 𝗜𝘁

𝗪𝗵𝘆 𝗠𝘆 𝗔𝗜 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗙𝗮𝗶𝗹𝗲𝗱 𝗔𝗻𝗱 𝗛𝗼𝘄 𝗜 𝗙𝗶𝘅𝗲𝗱 𝗜𝘁

I spent three weeks building an AI content feature. It worked on my laptop. It failed in production.

The feature broke often. Requests timed out. The API hit rate limits. The app crashed. Users hated the experience.

I tried simple fixes. I added retries. This flooded the API. I tried parallel requests. The API banned me. I tried simple caching. It did not work.

I needed a better way. I treated the AI API as an unreliable part of my system.

I changed my approach:

• Used async HTTP clients to stop server blocks.
• Added a circuit breaker to stop calls after three failures.
• Built a fallback cache for similar prompts.
• Limited parallel requests to avoid bans.

The results changed:

• Failure rate dropped from 15% to 0.5%.
• The app stopped crashing.
• My team stopped paging me.

Lessons for you:

• Use circuit breakers from the start.
• Use rate limiter libraries instead of guessing.
• Use background queues for slow tasks.
• Use vector DBs for caching.

How do you handle AI API limits? Share your patterns in the comments.

Source: https://dev.to/__c1b9e06dc90a7e0a676b/why-my-ai-feature-kept-failing-and-how-i-fixed-it-174b