Explore the integration of AI in API development through SDK generation from OpenAPI, enhancing code reliability and simplifying API consumption and adoption.
CoPilots or AI Coding Assistants are increasingly popular tools among developers, but how reliable are they when it comes to integrating APIs? While their semantic limitations are well known, can these tools still generate usable code with improved prompt engineering? Can they grasp workflows and chain API calls effectively? Moreover, are they capable of easing developer onboarding by accurately answering queries?
In this talk, Adeel addresses these questions and shares insights from building an API CoPilot, where his team trains AI on a traditional code generator to minimize hallucination issues. He discusses the steps involved in API consumption and integration, analyzing with examples where generative AI can be useful and where traditional code generation techniques are more effective. The talk concludes with a demo of creating a user playlist using the Spotify API, first without AI assistance and then with generative AI.
The key topics Covered in the video are:
- [00:10] English proverbs to understand Determinism and Hallucination challenges of Generative AI
- [03:00] AI code generation problems found in researches at Stanford and Purdue universities
- [04:48] The API-first Unicorns and common techniques of API adoption
- [06:41] Understanding the 3 steps of API consumption
- [08:41] Demo: API consumption steps without using AI
- [12:25] Demo: API consumption steps with the help of AI
- [14:33] Why is Gen-AI useful in the steps 1 and 3?
- [15:04] Why in the step 2, a traditional code generator is the best choice?
- [16:19] How SDKs abstract away the learning/coding of the step 2: API access code
- [17:25] A way to resolve AI's Hallucinations problems while generating API integration code
- [18:30] Training AI on deterministically generated code to help with dynamic queries and code generation
