Creating TypeScript Functions for Retrieving Users from REST APIs: A Generative AI Case Study

Generative AI is revolutionizing the way developers approach programming by providing intelligent assistance and automation throughout the coding process. With the power of advanced language models and machine learning (ML) algorithms, generative AI can understand the context and intent behind a programmer’s code, offering valuable suggestions, completing code snippets, and even generating entire functions or modules based on high-level descriptions. This technology empowers developers to focus on higher-level problem-solving and architecture, while the AI handles the tedious and repetitive aspects of coding.

One of the key advantages of large language models (LLMs) in programming is their ability to learn from the vast amounts of existing code and programming patterns they were trained on. This knowledge allows them to generate context-aware code, detect potential bugs or vulnerabilities, and offer optimizations to improve code quality and performance.

In a recent collaboration, the AWS Generative AI Innovation Center worked with SailPoint Technologies to build a generative AI-based coding assistant that uses Anthropic’s Claude Sonnet on Amazon Bedrock to accelerate the development of software as a service (SaaS) connectors.

SailPoint specializes in enterprise identity security solutions, with over 3,000 enterprises worldwide using their products to defend against identity-centric cyber threats and manage access to applications and data. Their products, designed to manage and secure access for users both inside and outside an organization, provide comprehensive identity governance capabilities to enforce security policies and compliance requirements.

SailPoint’s connectors, like the one we focus on in this post, interface with various SaaS applications to retrieve account and access information from an identity security standpoint. The specific connector we’re looking at involves listing users from a SaaS application via a REST-based web API.

To generate the ListUsers function for this connector, we follow a prompt chaining technique using Anthropic’s Claude Sonnet on Amazon Bedrock. This approach involves breaking down the complex problem into a series of manageable steps, such as parsing the data model of the API response, generating a user flattening function, understanding the pagination scheme, and finally generating the ListUsers function.

By structuring the task in this way, we ensure that the LLM understands each step before moving on to the next, enhancing the accuracy and proficiency of the code generation process. This method allows for iterative optimization of intermediate steps and enables the LLM to make complex decisions, such as selecting a pagination scheme based on the API specifications provided.

The output of this process is a fully functional ListUsers function that retrieves a list of users from the specified API, paginating through the results as necessary. The automated code generation significantly accelerates the connector development process, reducing development time and simplifying the onboarding process for new customers.

In conclusion, the AI-powered solution for generating connector code demonstrates the potential of generative AI in streamlining the integration with REST APIs. By automating the creation of connectors from API specifications, developers can rapidly build connections to any REST API, saving time and accelerating the integration process. This technology opens up new possibilities for leveraging APIs and enhances the efficiency and effectiveness of API integration efforts in today’s digital landscape.