How to use langchain output parsers with LLM chain| Tutorial:40

#ai #llm #langchain #openai #learntocode2023 The text is a comprehensive guide by Ronnie on "Total Technology Zone" for Tutorial 40, focusing on using LLM Chain with structured output parsers for generating company taglines based on descriptions. Here’s a summary of the key points covered in the tutorial:

1. **Introduction to LLM Chain and Output Parsers:** Ronnie introduces the concept of using LLM Chain in conjunction with structured output parsers, a follow-up to the previous tutorial's focus on output parsers with Lang chain agents. This tutorial is designed to be coding-intensive with minimal theoretical discussion to keep it concise and engaging.

2. **Setting Up the Environment:** Ronnie begins by setting up the coding environment in VS Code, including importing necessary modules from Lang chain and defining the LLM (Language Model) using the GPT (Generative Pre-trained Transformer) model with a specified temperature setting for response generation.

3. **Creating the Template:** A template string is created for generating a company tagline based on a description. The template is designed to take a company description as input and generate a unique tagline along with a popularity score from 1 to 100 based on the model's analysis.

4. **Prompt and Response Schema:** Ronnie demonstrates how to create a prompt using the template string and how to define a response schema that includes variables for the tagline and rating. This schema guides the structured output parser in formatting the LLM's response.

5. **Implementing LLM Chain:** The tutorial progresses to the implementation of LLM Chain, where Ronnie outlines how to use the prompt and structured output parser together. This combination allows for the generation of responses in a structured format directly from the LLM without the need for an intermediary agent.

6. **Execution and Examples:** Ronnie executes the code with example company descriptions to demonstrate how the system generates taglines and assigns them popularity scores. The tutorial illustrates the process of extracting specific pieces of information (e.g., tagline) from the structured response.

7. **Conclusion and Future Tutorials:** The tutorial concludes with Ronnie explaining the significance of the demonstrated concepts for project and product development. He hints at future tutorials focusing on advanced parsing techniques, emphasizing their importance for large-scale application development.

8. **Call to Action:** Ronnie encourages viewers to subscribe to the channel, engage with the content through comments, and share the videos. He highlights the importance of community support in growing the channel and enhancing content discovery.

In summary, Tutorial 40 is a practical demonstration of using LLM Chain with structured output parsers to generate structured responses, such as company taglines, directly from language models, showcasing an efficient approach to handling and formatting AI-generated content.

[Motivationalvideos]

Sir I have to get watsapp API system developer which has automatic message on dream11 then came on through watsapp of link given on mumber message

[SagarPatel-hg]

Hi Thanks a lot for tutorial. Can we store this in some variable that can be further been used?

[softdev.alamin]

Why your intro sound too lound then your voice over ?