Output Parsing, Validation, and Application Reliability

Generating text with Large Language Models is often just the first step. The raw output can be inconsistent or formatted in ways that are difficult for software to use directly. Integrating LLM responses reliably into applications requires specific techniques to manage this variability.

This chapter focuses on processing LLM output to build dependable applications. You will learn methods to guide models toward producing structured data, parse responses into usable formats (like JSON), validate the structure and content of the results, and implement strategies such as retry mechanisms and content filtering to handle errors and improve application stability. We will cover using output parsers, data validation libraries, and approaches for managing situations where the LLM output doesn't meet expectations.