Gaurav Tripathi

I built an AI assistant that transformed how our team and customers interacted with our API documentation. The problem was that finding and understanding the right APIs across our extensive documentation was time-consuming. I wanted to enable natural language queries like "how do I create a new user and assign them to a store?" rather than forcing people to search through multiple documentation pages.

As a solution, I implemented OpenAI's GPT models with three key components:
• Data preparation: I extracted and processed our API documentation, chunking it appropriately to maintain context while fitting within token limits.
• Semantic search: When a user asks a question, the system calculates similarity metrics between the query and our documentation chunks to identify relevant information.
• Response generation: The system prompts GPT with both the query and relevant documentation chunks, with specific instructions to only answer based on the provided information and acknowledge when it doesn't have sufficient data.

Early metrics suggested a 40% reduction in time spent finding the right APIs for specific tasks. This solution has evolved from a proof of concept to a product feature that adds measurable value for our customers.