I've been shipping AI-powered features for the last year. Here are the patterns that actually moved the needle:

1. Structured output schemas — Stop parsing free-text LLM responses. Define a JSON schema upfront and force the model to conform. Cuts error handling code by 80%.

2. Prompt chaining over mega-prompts — Break complex tasks into 3-4 small, focused prompts instead of one massive one. Each step validates before the next runs. Way more reliable.

3. Semantic caching — Hash similar inputs and cache responses. Most AI API calls are variations of the same query. This alone cut my API costs by 40%.

4. Fallback routing — Don't rely on one model. Route to a cheaper/faster model for simple tasks and escalate to the heavy hitter only when needed.

5. Async batch processing — Queue non-urgent AI tasks and process them in batches. Better rate limit management, lower costs, same results.

I package all of these into reusable templates and automations inside Autopilot Labs. New tools drop every week.