Parsing structured outputs from Large Language Models (LLMs) is a crucial skill when developing AI-powered applications.

It allows developers to extract precise, predictable information from models, transforming unstructured text into structured data.

In LangChain, there are various methods for parsing structured outputs from LLMs, each with its own strengths and use cases.

In this audio, we’ll explore three primary techniques: the with_structured_output method, the PydanticOutputParser class, and the StructuredOutputParser class.

Parsing LLM Structured Outputs in LangChain: A Comprehensive Guide

Juan Carlos Olamendy