Author(s): Ajit
Originally published on Towards AI.
Getting Structured Output from LLM: Guide to Prompts, Parsers, and Tools
Large language models (LLMs) like GPT-4 are incredibly powerful at generating human-like text, but they often produce unstructured, free-form output. Many real-world applications require data in a strict format (for example, JSON or CSV) for reliable integration into software or databases. For example, we want the model to output a JSON object with specific fields (such as name, ageOr items) or a well-formatted list. Structured output enables us to program directly with LLM output and avoid brittle text parsing. LLMs generate text token-by-token according to learned probabilities, so they often require special techniques or tools to guide them to output perfectly structured data.
In this article, we explore the main approaches – expedited engineering, output parser, OpenAI function-calling, and structured-output features – for extracting structured data from LLM, addressing their pros, cons, and providing example code to illustrate these approaches.
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Published via Towards AI
