The author also highlights two key criteria for these frameworks: their ability to handle or prevent malformed JSON and the visibility and control over the prompt. The article includes example codes for each framework, demonstrating how they can be used. The author emphasizes that the best way to get structured data from an LLM is to craft a prompt designed to return data matching a specific schema, a feature not always available due to hardcoded templates in most frameworks.
Key takeaways:
- The article discusses the challenges of getting structured output from Language Learning Models (LLMs), particularly in the form of JSON, and presents different frameworks that can help solve this problem.
- It emphasizes the importance of handling or preventing malformed JSON, with two techniques being parsing the malformed JSON or constraining the LLM's token generation to guarantee valid JSON production.
- The article also highlights the need for full control over the prompt in LLMs, as prompts are the means to program LLMs to provide specific output.
- Several examples of different frameworks (BAML, Instructor, TypeChat, Marvin) are provided, with code snippets demonstrating how they can be used to extract structured data from LLMs.