Large language models are a variety of artificial intelligence that has been trained to understand and generate human language. These models are used in many fields, including programming, to help humans accomplish daily tasks.
To communicate with the model effectively, you need to understand how to form requests properly. Chain of thought prompting is one of the most efficient techniques when interacting with LLMs.
In this article, you will learn what chain of thought prompting is, how to implement it and what strategies to use to overcome challenges associated with it.
What is prompting?
LLMs are trained on vast datasets to understand and generate human-like text. Emerging abilities of large language models rely on prompts-input cues that initiate and guide the text generation process. A prompt can be a simple sentence, a question, or even a keyword that sets the context and prompts the model to generate relevant content. For programmers and tech professionals, understanding the concept of prompting is essential to leverage LLMs effectively.
There are several approaches to prompting LLMs:
Single-prompt approach
This technique involves providing a straightforward prompt to the LLM, such as “Summarize this…
