Leveraging TypeScript and AI: Bridging Language Models with Traditional Apps
Join Daniel Roser, Program Manager on TypeScript, as he explores how types can guide and validate language models to bridge the gap between natural language and code.
- 1. Daniel Roser is discussing TypeChat, an experimental library for making AI tools and language models accessible to all engineers.
- 2. He compares using chatGPT, which provides continuous natural language output, with traditional apps that require precise data.
- 3. The challenge lies in bridging these two worlds to make new AI tools usable by every engineer.
- 4. Roser demonstrates this issue using a simple app that takes user input and formats it as items with venue names and descriptions.
- 5. To use the output from chatGPT or other language models effectively, developers often need to "pamper" the models by adhering to specific formatting patterns.
- 6. Relying on natural language output can lead to parsing issues, as it's difficult to anticipate every possible format a model may produce.
- 7. One workaround is to ask the model for JSON output, which provides more consistent data. However, this method alone does not guarantee that your app will receive the necessary data.
- 8. Using types (e.g., TypeScript interfaces) can help guide language models by providing clear expectations for user input and intended outcomes.
- 9. By combining types with a sufficiently trained AI model, developers can create a system that bridges natural language and code.
- 10. Types not only guide the model but can also validate results to ensure well-typed data is returned.
- 11. This approach simplifies development by providing error messages when validation fails, allowing for repair and re-running of the query.
- 12. TypeChat is an npm library that bundles these features together, making it easy to guide language models and validate results.
- 13. The library allows developers to work with more complex examples, such as defining schemas and inferring user intent from input.
- 14. Roser demonstrates the use of TypeChat by creating a coffee shop app that takes user orders and validates input through types.
- 15. By using types, developers can anticipate failure scenarios and provide opportunities for recovery within their applications.
- 16. This schema-driven approach is powerful as it allows developers to define expectations and constraints upfront, simplifying the development process.
- 17. TypeChat also supports generating code based on defined methods and types, making it suitable for more complex use cases.
- 18. Roser mentions that when using language models to generate code, safety constraints are necessary to ensure the generated code remains within expected bounds.
- 19. A fake language is used in TypeChat to generate JSON-based outputs, which can then be translated into a typed program within memory.
- 20. This approach ensures only approved methods are called and that inputs from prior steps match up with defined types from an API.
- 21. TypeChat has been experimentally adapted for use with languages like Python, achieving similar results as in the original TypeScript implementation.
- 22. Roser encourages attendees to try TypeChat, reach out for assistance, and bridge the gap between cutting-edge AI tools and traditional apps.
- 23. He can be found at the Microsoft booth during the conference.
Source: AI Engineer via YouTube
❓ What do you think? What are your thoughts on the ideas shared in this video? Feel free to share your thoughts in the comments!