LLMs: Beyond Chat

Using Pydantic and Instructor with OpenAI GPT-4o to use the LLM as a software device for implementing different tasks.

Integrating Large Language Models (LLMs) effectively into software workflows demands more than handling unstructured text. Raw LLM output lacks the predictability and format consistency required for reliable system integration, hindering automated processing and validation. The key challenge lies in compelling LLMs to produce structured, validated data suitable for direct use in applications. How can we achieve this shift from ambiguous text to dependable, structured output?

github.com/intellectronica/llms-beyond-chat offers a practical exploration of this approach. It provides a hands-on tutorial demonstrating how to utilise LLMs, specifically OpenAI’s GPT-4o, as sophisticated “software devices” capable of performing defined functions within your applications.

Learn how to leverage libraries like Pydantic and Instructor to compel LLMs to return predictable, validated data structures rather than just unstructured text. This is crucial for reliable application integration.

The repository includes:

  • A complete notebook with all solutions and outputs (llms-beyond-chat.ipynb).
  • A practice version (llms-beyond-chat---practice.ipynb) allowing you to code along and solidify your understanding.
  • A TypeScript/JavaScript port (llms-beyond-chat-typescript.ipynb) demonstrating the concepts with the Vercel AI SDK, catering to the web development community.

If you’re a developer or AI practitioner looking to move beyond basic LLM interactions and explore techniques for building more robust, predictable, and integrated AI-powered features, this repository is an excellent starting point.

Ready to unlock the next level of LLM potential?

  • Clone the code, and start experimenting:
  • Watch the video tutorial for a detailed introduction and guided exercises:Play