At first glance, OpenAI's API is a game-changer for adding intelligence and generative capabilities to applications. We were eager to push the limits of what our apps could do, aiming for a seamless integration. However, the real test came when we started to use the API for our specific needs, revealing the limitations beneath the surface, and the need for a much more robust platform, particularly in terms of the developer experience.
The Challenge of Comprehensive Testing
One of the first obstacles was the sheer amount of testing required. With a variety of prompts to evaluate in numerous scenarios, and with any given user input injected into the prompt, our goal was to ensure quality outputs for any given scenario. While the "Playground" feature was a helpful start, it quickly became clear that we needed a more robust system for real-time issue detection and performance tracking. Maintaining accuracy, safety, clarity, and utility for all of our prompts showed us a real need for a more comprehensive testing suite.
The Complexity of JSON Validation
As we moved beyond basic text outputs, the task of validating JSON responses for accuracy and consistency became crucial. The "JSON mode" provides a nice foundation for recognizing valid objects, but it doesn't guarantee their correctness across all instances, a gap that we had to bridge with additional testing.
RAG: A Complex Puzzle
Introducing a Retriever-Augmented Generation (RAG) feature brought another layer of complexity. It's an innovative process that enhances AI interactions, like creating more fine-tuned AI results. Consider the case of an AI Non-Player Character (NPC) that needs contextual information about its world; document retrieval can facilitate this. However, often we wanted to omit information—for example, an NPC gradually learning more about the world as the player advances in the game. Managing this evolving state and linking it to document searches became a logistical and technical nightmare.
Budgeting: An Opaque Journey
Perhaps one of the most intimidating aspects was managing and forecasting costs. The expenses tied to using OpenAI's API—such as tokens, storage, and additional services—required meticulous planning and monitoring. Venturing into production without a clear financial map was like navigating without a compass, challenging but essential for moving forward.
Moving Forward: A Call for Simplification
Reflecting on these challenges, it's clear that while we've managed to navigate through them, there's a strong need for a more streamlined approach. Each hurdle highlighted areas for improvement, particularly in making the API more accessible and manageable for developers.
Meet Jixi
Jixi is a development platform that makes creating AI software intuitive, fast, and secure. We built the platform we wished existed during our AI development journey. A full development SDK, a drag-and-drop file system with automatic embedding and retrieval, a full analytics suite, and AI confidence scores for creating generated response we can trust. Jixi is available to try, here.
