Chain of Thought Prompting

News

Midjourney v 6 is now available. The MidJourney V6 features enhanced prompt accuracy, improved coherence, and, precisely, text generation capabilities. The latest model shows significant improvement aided by feature updates such as longer prompt length, more control over color, shading, and text, and the ability to fine-tune the output through a conversation.

Anthropic, the makers of Claude AI are seeking $750 million in additional funding. Anthropic is in discussions to raise $750 million in a funding round led by Menlo Ventures. The company already has investments from Google and Amazon, and the valuation before the funding round came to $18.4 billion.

Apple is creating a set of generative AI tools that can rival OpenAI’s Chatgpt.

Stanford students have created an AI that can accurately tell where your photo was taken. There are likely good and bad aspects of this development. It could be used to expose data about individuals they had not intended to share. On the other hand, it could also be used to identify locations of old photos.

The field of natural language processing has seen significant advancements in recent years, with large language models(LLMs) becoming increasingly sophisticated in their ability to understand and generate human-like text. However, these models can still struggle with certain tasks, particularly those that require a deep understanding of context or the ability to generate complex, coherent responses. This is where the chain of thought prompting can come in - a technique that has been shown to significantly improve the performance of large language models on a variety of tasks.

By providing additional context and guidance to the model, chain of thought prompting enables it to generate more detailed, relevant, and coherent responses. As such, it is a valuable tool for unlocking the full potential of large language models and enabling them to perform at their best.

Chain of thought prompting involves maintaining conversational context to connect a series of statements or questions into a coherent line of discussion. Rather than treating each prompt separately in isolation, I will remember what we have discussed previously and use that context to provide a relevant response that builds upon what you have asked before.

For example, by expanding on your initial question an AI provides additional details through follow-up responses while keeping the overall flow of the conversation going. This allows for deeper exploration of ideas with related prompts.

Examples of Chain of Thought Prompting in Action

To demonstrate the effectiveness of chain of thought prompting, let's look at some examples of how it can be used to improve the responses of a large language model.

With chain-of-thought prompting

User: I need help writing a cover letter for a job application. First, I want you to write an introduction that explains my qualifications and why I'm interested in the position.

User: Next, I want you to describe my relevant experience and explain how it aligns with the company's needs.

User: Finally, I want you to summarize my strengths and explain why I'm the best fit for the job.

By providing step-by-step instructions through the chain-of-thought prompt, the user can guide the AI model to produce a well-structured and personalized cover letter that is tailored to their specific job

Without chain-of-thought prompting

"Please write a cover letter for a job application."

This prompt provides a general request for a cover letter without specifying any specific details or instructions. The AI model will likely produce a generic cover letter that may not be as personalized or tailored to the user's needs as the one generated using chain-of-thought prompting.

There is also asking the AI bot to take on a role, or persona. I’ll cover this more in the next issue, but a brief explainer follows:

Taking on a role (also called a "persona") is a slightly different technique than chain-of-thought prompting. In persona prompting, the user asks the AI to adopt a specific character or persona, such as a customer service representative, a teacher, or a doctor. This can help the AI better understand the context of the conversation and provide more personalized responses.

However, taking on a role is not necessarily the same as providing step-by-step instructions through chain-of-thought prompting. While both techniques can help the AI produce more tailored responses, chain-of-thought prompting provides more detailed instructions for the AI to follow, while taking on a role provides more context and background information. Both techniques can be useful in different situations and can be combined for even more personalized responses.