Meta Reveals 7 Prompt Techniques You Need To Know

Meta, the company behind Facebook and Instagram runs one of the most popular large language models called Llama 2 and they have now released a prompt engineering guide that we can use to get better prompts.
And since all large language models work in a similar way, we can use these for ChatGPT, Bard, Copilot and Claude.
This document is created for developers, so I simplified it for non-developers and I have 7 very actionable prompting techniques I wanted to share in this video.
Link to the guide: github.com/facebookresearch/l...
1 - Explicit Instructions
Detailed, explicit instructions produce better results than open-ended prompts:
* Stylization
* Explain this to me like a topic on a children's educational network show teaching elementary students.
* I'm a software engineer using large language models for summarization. Summarize the following text in under 250 words:
* Give your answer like an old timey private investigator hunting down a case step by step.
* Formatting
* Use bullet points.
* Return as a JSON object.
* Use less technical terms and help me apply it in my work in communications.
* Restrictions
* Only use academic papers.
* Never give sources older than 2020.
* If you don't know the answer, say that you don't know.
2 - Zero-Shot Prompting
Large language models like Llama 2 are unique because they are capable of following instructions and producing responses without having previously seen an example of a task. Prompting without examples is called "zero-shot prompting".
Examples: Prompt: "Write a blog post about the latest trends in social media marketing for small businesses."
Prompt: "Explain the basics of using generative AI in digital marketing in a simple, easy-to-understand way."
3 - Few-Shot Prompting
Adding specific examples of your desired output generally results in more accurate, consistent output. This technique is called "few-shot prompting".
Example: Content Creation: Blog Post about Social Media Marketing for Small Businesses
* Example 1: "Here's a blog post about how small businesses can leverage Instagram for growth."
* Example 2: "This is an article discussing the benefits of Facebook advertising for local businesses."
* Prompt: "Now, write a blog post about the latest trends in social media marketing for small businesses."
Example: Educational Material: Basics of Generative AI in Digital Marketing
* Example 1: "Here's a simple explanation of how AI is used in data analysis."
* Example 2: "This is an easy-to-understand overview of AI in customer service automation."
* Prompt: "Explain the basics of using generative AI in digital marketing in a simple, easy-to-understand way."
4 - Role Prompting
Llama 2 will often give more consistent responses when given a role.
Example: Prompt: "As a social media influencer with a large following in the health and wellness space, suggest creative ways to use Instagram for building a brand presence."
Prompt: "Imagine you are a successful entrepreneur who has built multiple businesses. Share your top five strategies for effective time management and productivity."
5 - Chain-of-Thought
Simply adding a phrase encouraging step-by-step thinking "significantly improves the ability of large language models to perform complex reasoning
Example: Add this to any prompt ”Let's think through this carefully, step by step.”
6- Self-Consistency
Self-Consistency introduces enhanced accuracy by selecting the most frequent answer from multiple generations.
Think of self-consistency in AI like asking a group of experts the same question and then seeing what most of them agree on. The AI gives several answers to one question, and then we look for the answer that pops up most often. It's like believing what the majority says, assuming that if most experts (or in this case, AI responses) agree on something, it's probably the most accurate or reliable answer.
Example: Prompt: "Provide five separate recommendations for pricing a new AI-powered educational tool for entrepreneurs. Determine the most frequently suggested price range from these recommendations."
7 - Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is a technique where AI enhances its responses by first gathering information from a large database of texts and then using that information to answer questions or generate content.
Prompt: Research the latest models of electric cars released in 2024. Compare their features, prices, and consumer reviews, and suggest the best options for city commuters.
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[@SkillLeapAI]

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[@DianasReviews]

Thank you for simplifying it for us ☺

[@path1024]

I don't know if "reveal" is the most accurate word here since these are things anyone who's done this for more than a week has known for a while.

[@BlkDiesel]

I thank you for SHARING this information, but if you have been familiar with prompt engineering for the past 6-8 months or so, like myself and many others, these prompting techniques aren't NEW AT ALL. I've been familiar with these for about 9 months now. I appreciate the share anyway. Be Elite Today People!

[@SkillLeapAI]

Yea I didn’t say there were new techniques in the title. The new icon only refers to the fact that this documentation is new. Lot of my subscribers have less experience in prompting and haven’t seen all of these before

[@BlkDiesel]

@@SkillLeapAI Understood, thanx for the clarification.

[@abdelkaioumbouaicha]

📝 Summary of Key Points:
📌 The guide introduces seven prompting techniques for developers working with large language models like Llama 2. The first technique is explicit instructions, where developers provide detailed and specific prompts.
🧐 The second technique is zero-shot prompting, where no previous examples are given to the model. The third technique is few-shot prompting, which involves providing examples of the desired output to give the model context.
🚀 The fourth technique is role prompting, where developers specify a role for the model to assume. The fifth technique is chain of thought prompting, which helps the model perform complex reasoning.
🌐 The sixth technique is self-consistency, where the model generates multiple answers to the same question and selects the most commonly agreed-upon answer. The final technique is retrieval augmented generation (RAG), which instructs the model to research external sources for more accurate information.
💡 Additional Insights and Observations:
💬 "Explicit instructions" technique involves using formatting like bullet points or JSON objects to effectively communicate requirements.
📊 No specific data or statistics were mentioned in the video.
🌐 Additional resources and examples are available in the video description.
📣 Concluding Remarks:
The guide released by Meta provides developers with seven effective prompting techniques for working with large language models. These techniques, such as explicit instructions, zero-shot prompting, and retrieval augmented generation, can enhance the performance and accuracy of AI chatbots and language models. Developers can customize and combine these techniques to create more effective prompts.
Generated using TalkBud

[@user-ow5qg6wx7e]

Nothing new. It is already from around 8 months on Open AI documentation.

[@SkillLeapAI]

Prompt Guide from Meta is new. Never said the prompt techniques are new. But most people aren't using the fundemental techniques and that's why I think they just now released this document.

[@user-ow5qg6wx7e]

@@SkillLeapAI it is not new. They just copy and paste techniques that are used for a long time.