Maximizing Developer Productivity with Custom GPTs

Paf adopted ChatGPT Enterprise company-wide to build an environment where engineers can accelerate everyday development tasks. The engineering team has directly created and uses more than 85 custom GPTs to support specific use cases.

After reviewing various AI models, GPT-4 was adopted as the final solution after proving to be 25% more accurate than competing models such as LLAMA and Claude, while also being cost-effective.

To streamline the development process, the engineering team uses the following specialized coding GPT suite:

• Swagger GPT: Converts Swagger JSON API definitions into TypeScript service endpoints that conform to Paf's standards
• TypeScript GPT: Writes backend service code based on endpoint definitions
• GraphQL Nexus GPT: Generates GraphQL Nexus schemas that interact with the frontend
• Relay GPT: Generates React Relay hooks for backend communication
• React GPT: Writes React components using Paf's style guide and core component library

By chaining these custom GPTs together, the team prevents model overload and suppresses hallucination. This allows developers to generate standardized application flows and APIs almost automatically, enabling them to focus on higher-level architectural work such as system design rather than simple syntax errors.