MASTER the Prompt: TOP 5 Elements for Reusable Prompts, AI Agents, Agentic Workflows
4,159
Published 2024-06-17
Ever wondered which parts of your prompts ACTUALLY MATTER? Let's simplify the sea of prompt engineering tips and tricks with this concise guide on the five essential elements that gives you 80% of the results with 20% of the effort. I've crafted thousands of prompts, and today, I'm sharing the key elements that consistently deliver top-notch outcomes with minimal effort.
🚀 Discover the five crucial components: model, purpose, variables, examples, and output. These elements form the backbone of effective prompt engineering, helping you achieve 80% of the results with just 20% of the effort.
đź”— These elements enable composability between prompts, allowing you to chain together outputs and inputs to build powerful prompt chains. By focusing on the underlying technology and understanding the prompt at its core, you'll be well-equipped to create AI Agents and agentic workflows.
🔥 In this video, we break down each element with clear examples and practical tips:
Model: Learn why the model you choose has the most significant impact on your prompt's performance.
Purpose: Understand how a clear goal enhances your prompt's effectiveness.
Variables: Master the use of dynamic and static variables to make your prompts adaptable and reusable.
Examples: See how concrete examples can guide your AI to produce the exact output you need.
Output: Explore the importance of structured outputs, like JSON, for building reliable and consistent AI workflows.
🛠️ We'll showcase practical applications of these elements through detailed examples, including a Nuxt.js component and a comprehensive Omnicomplete prompt. Whether you're a developer, AI enthusiast, or product builder, this video is packed with actionable insights to enhance your prompt engineering skills.
🌟 Hit the like and subscribe for more tips on AI agents, agentic workflows, and prompt chains. Stay ahead of the curve and stay plugged into the latest models like GPT-4o, Gemini 1.5 Pro, and other state-of-the-art models to use their full potential for your prompts, AI Agents, and agentic workflows.
Keep prompting, keep building and stay ahead of the curve.
đź”— Resources
Llama-3 70b Omnicomplete:    • Llama-3 70b OMNI-complete: AUTO Impro... Â
LLM OS:    • Agent OS: LLM OS Micro Architecture f... Â
7 Prompt Chains:    • 7 Prompt Chains for Decision Making, ... Â
đź“– Chapters
00:00 Maximizing Prompt Value
00:34 The Five Key Elements of Prompts
01:24 E1 - Model - Why the Model Matters Most
01:59 E2 - Purpose - Defining Your Goal
02:25 E3 - Variables - Dynamic and Static
03:32 E4 - Examples - Concrete Examples for Clarity
05:49 E5 - Output - Structured and Reliable JSON
07:16 Concise, valuable, reusable prompts
08:00 Building Prompt Chains and AI Agents
08:56 Recap - The 80-20 of Prompt Engineering
09:48 Real Example Prompt - OmniComplete - Static Variables
12:20 Real Example Prompt - Nuxt / Vue Component
13:53 Top 5 Elements for Reusable Prompts
14:30 Focus on the groundwork - the prompt
#promptengineering #aiagents #aiengineering
All Comments (21)
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honestly don't know why you've been stuck at ~14k subs for ages... content is gold. consistently.
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What tool is that you are using for the prompt stuff
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Model, purpose, variables, output example, JSON format. Thank you very much 🙏
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Finally! Back to the good stuff. Much appreciated!
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There are a lot of videos on YouTube about AI Agents and Prompts. Dan, your videos are spot on every time. Thank you, Sir!
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I think a great follow up video would be one that discusses JSON in more depth and how to work with JSON that would be an important move. I understand JSON but it still has some mysteries that I would like to clear up thanks for a good video.
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This video is gold!
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Good video. Shows new concepts to use in code, I especially liked the last 2 examples where it was all used together. Thank you IndyDevDan
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Pretty awesome indeed and great value / knowledge
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This content is gold. Thanks a lot!
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Beautiful. Appreciated, sir.
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Thanks for sharing, this is valuable alpha in the space!
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This is awesome 🥳
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Really enjoying your content, thank you. Like others, I’d like to know more about the tool you’re using to present in this video.
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I am so sorry to ask, what platform is this? Or a Github repo? Thank you this is one of the best videos
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Does all LLM model understand this !?
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awesome, totally agree with these 5 elements. Do you think langchain or other libraries cover these elements well?
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New to the channel, new to coding. What is this interface you are using?
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Can you talk about DSPy ?
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I've been struggling to extract code from LLM output. I think EXAMPLES you mentioned would solve them. Thanks a lot! Can we use aider with non openai models? I couldn't make it work with Databricks models
