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Hands-On LangChain for LLM Applications Development: Output Parsing
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Hands-On LangChain for LLM Applications Development: Output Parsing

Last Updated on March 4, 2024 by Editorial Team

Author(s): Youssef Hosni

Originally published on Towards AI.

When developing a complex application with a Language Model (LLM), it’s common to specify the desired output format, such as JSON, and designate particular keys for organizing the data.

Let’s consider the chain of thought reasoning method as an illustrative example. In this method, the LLM’s thinking process is represented by distinct stages: “thought” indicates the reasoning process, “action” denotes the subsequent action taken, and “observation” reflects the learning acquired from that action, and so forth. By crafting a prompt that directs the LLM to utilize these specific keywords (thought, action, observation), we can effectively guide its cognitive process.

In this article, we will cover coupling the prompt with a parser that allows for the extraction of text associated with certain keywords from the LLM’s output. This combined approach offers a streamlined means of specifying input for the LLM and accurately interpreting its output.

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