LLM Text Generation (3.2)

In this video, we explore the task of text generation using Large Language Models (LLMs), similar to how we've previously generated code. Instead of using a conversational chat style, we demonstrate how to send prompts to LangChain and receive the generated text. The video includes a step-by-step guide on setting up the code necessary to query an LLM for text generation, providing practical examples and explaining the use of parameters like temperature to control the model's creativity and randomness.
We then delve into different text generation patterns, including zero-shot, one-shot, and few-shot prompts. Each pattern is explained with examples, showing how varying the amount of information given to the LLM can impact the quality and relevance of the generated text. Whether you're creating content with minimal guidance (zero-shot), providing a single example (one-shot), or using multiple examples to guide the model (few-shot), this video equips you with the knowledge to effectively generate text for various applications.
Code for This Video:
github.com/jef...
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Thank you Jeff !