Structured Output from Large Language Models
Obtaining structured output, such as JSON, from large language models (LLMs) is crucial for robust applications. While simply prompting for JSON can lead to errors, more reliable methods exist. Native Structured Output (NSO) features, offered by providers like OpenAI, guarantee 100% accuracy through smarter training and guided token choice. This involves restricting the model’s token selection to ensure the output conforms to a specified schema. These schemas, which can include detailed rules for data types like strings (e.g., RegEx patterns, enums) and numbers (e.g., min/max values), are stored by the providers to optimize performance. Frameworks like LangChain also facilitate structured output by providing tools and architectures to integrate LLMs effectively, enabling the creation of agents that adapt and generate predictable data [medium.com](https://medium.com/@chanyatfu/the-developers-field-guide-to-structured-llm-output-7f484134778b), [js.langchain.com](https://js.langchain.com/v0.2/docs/how_to/output_parser_structured/). This approach is essential for building dependable applications that rely on clean, predictable data from LLMs [js.langchain.com](https://js.langchain.com/docs/concepts/structured_outputs/), [json-schema.org](https://json-schema.org/draft-07).
Intéressant ce truc de restreindre la sortie du modèle aux tokens acceptés, j’imagine que ça doit réduire grave les erreurs de parsing.
Grave, ça optimise carrément le parsing en évitant les sorties foireuses.
Le truc du NSO qui garantit 100% d’accuracite me bluffe, c’est vraiment un game changer pour éviter les bugs dans les apps.
100% d’exactitude grâce au NSO, ca change tout pour les applis critiques. J’avais pas idée que c’était aussi contraint avec les tokens.