Meta Reveals 7 Prompt Techniques You Need To Know

[@SkillLeapAI]

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[@DianasReviews]

Thank you for simplifying it for us ☺

[@classicmetaldude]

SUPER COOL...I've been bringing the questions up to META AI, asking the definitions of what I want at the end, then once it replies with the definition, I'll say, "Make an image according to that definition." And it restricts the AI into a more closely made end to what I wanted...

[@D4MAGEINC]

Meta will do rag by just telling it can. I have a prompt called feature update and I give it a list of features that I wanted to update and one of them is data retrieval as well as scraping websites. Thanks for the video thumbs up subscribed

[@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

[@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.

[@szambalamba]

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.

[@szambalamba]

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