What we learned building an AI Assistant Feature #shorts #ai
Science & Technology
Introduction
In developing an AI assistant feature, we encountered several challenges, particularly in the area of software engineering related to crawling API documentation. Initially, our plan appeared straightforward: it was essentially a two-step process. The first step involved downloading the API documentation, while the second step was to employ a large language model (LLM) to navigate through and interpret the information.
However, as we delved deeper into the actual implementation, it became clear that the complexity involved was much greater than we had anticipated. Building a connector into an API is not as simple as it might seem. The steps required are multifaceted and often convoluted.
First, we need to access the web page that details how to connect to the desired API. This page typically contains extensive documentation filled with important information that must be thoroughly read and understood. A critical aspect involves decisions around how to authenticate. We need to correctly provide our credentials so we can log into the API.
Moreover, we discovered that the set of endpoints offered by the API is not only diverse but also highly specialized, addressing a wide array of use cases. The variability in the documentation is astonishing; it is as if each provider has created a unique labyrinth instead of adhering to a common standard. This vast array of information, expectations, and technical requirements makes it challenging to implement a reliable strategy for integration.
In summary, our initial oversight was underestimating the degree to which the unique challenges of software engineering would complicate the project. The lessons learned during this process have been invaluable, as they highlight the importance of thoroughly accounting for both the technical and practical challenges involved in API integration.
Keywords
- AI assistant
- API documentation
- Software engineering
- Crawling
- Authentication
- Endpoints
- Use cases
FAQ
Q: What were the initial steps proposed for building the AI assistant feature?
A: The initial steps involved downloading the API documentation and using a large language model (LLM) to interpret the information.
Q: What unexpected challenges arose during the development process?
A: We underestimated the complexity involved in crawling API documentation and configuring the necessary software engineering components.
Q: Why is authentication considered a critical aspect in building API connectors?
A: Proper authentication is essential to ensure that we can securely log into the API and access the required endpoints.
Q: What variability did we find in API documentation?
A: API documentation is highly variable, with each provider offering unique structures and practices, making standardization difficult.
Q: What was the key takeaway from the development experience?
A: The experience underscored the importance of thoroughly accounting for both technical and practical challenges when integrating with APIs.