How to integrate AI into your product

Short summary

Most AI features get quietly removed within a year because teams bolt on LLMs to look modern rather than solve real problems. The winning approach is to find tasks with unstructured input, human tolerance for near-misses, and genuine tedium, then apply the lightest technique that works — prompting first, RAG second, fine-tuning rarely. Designing for the model being wrong with human-in-the-loop approval, grounded retrieval, and structured outputs is what separates a shipped feature from a one-time demo.

• •Start from user pain points, not from the model — look for unstructured input, tolerance for near-misses, and tedious manual work
• •Use the lightest technique that works: prompting first, RAG when you need your data, fine-tuning only as a last resort
• •Plan for wrong answers with human-in-the-loop review, grounded retrieval, structured output validation, and graceful failure

Generated with AI, which can make mistakes.