Hey there! If you're diving into the world of AI agents and automation systems, you've probably heard a lot about prompt engineering. But let's be real—most of the content out there is either too theoretical or doesn't focus on the practical aspects of prompting within AI agents and automation systems. In this guide, I'll break down everything you need to know about prompt engineering in a way that's both practical and actionable.
Why Prompt Engineering Matters
Before we get into the nitty-gritty, let's talk about why prompt engineering is so crucial. You've probably heard the phrase "English is the new programming language," and it's true. Non-coders can now build powerful applications and automations using plain English. However, communicating with Language Models (LMs) is different from communicating with humans. Efficiently communicating with LMs is the number one skill you need to build reliable AI systems. It increases the reliability, accuracy, and cost-efficiency of your system, especially when implementing these systems in businesses.
Key Concepts in Prompt Engineering
Conversational vs. Structured Prompting
- Conversational Prompting: This is what you do in ChatGPT, where you can go back and forth until you get the right output.
- Structured Prompting: In AI systems, you don't have the luxury of back-and-forth tweaking. You need to get it right with a single prompt. Structured prompting follows a framework and incorporates proven best practices like few-shot prompting, chain of thought, and markdown formatting.
Single Task Optimization
LMs are not good at performing multiple tasks. Always optimize prompts for one specific task. For complex tasks, use chain prompting to break down the task into smaller, manageable steps.
The Frameworks
I've broken down prompt engineering into three main frameworks: short structured prompting, long structured prompting, and agent prompting. Let's dive into each.
Long Structured Prompting Framework
This framework is ideal for complex tasks and includes seven sections:
- Role: Assign a role and qualities to the LM. For example, "You are a world-class CV formatter with expertise in reformatting CVs."
- Objective: Describe what needs to be done. Use sentences like "Your goal is to carefully read the original CV and reformat it into the new layout."
- Context: Explain why the task is important. For example, "This task is crucial for our recruitment firm."
- Instructions: Include all rules and output specifications. Predict what could go wrong and address it here.
- Examples: Provide input and output examples to increase reliability.
- Variables: Include any variables that will change depending on the task.
- Notes: Double down on important rules and best practices. LMs take the beginning and end of prompts more into account, so this section is crucial.
Short Structured Prompting Framework
For simpler tasks, you can use a reduced version of the long framework. This includes:
- Objective + Instructions: Combine the objective and instructions into one section.
- Examples: Provide at least one example.
- Variable: Include any necessary variables.
Agent Prompting Framework
Agent prompting is the hardest type of prompting because agents usually have multiple responsibilities. The framework includes:
- Role: Similar to the long structured prompt.
- Objective: High-level overview of the agent's responsibilities.
- Context: Explain why the task is important.
- SOP (Standard Operating Procedure): Break down the steps the agent needs to take.
- Instructions: Include important rules and communication guidelines.
- Tools and Sub-Agents: Describe what tools and sub-agents the agent has access to and how to use them.
- Examples: Provide input and SOP examples.
- Notes: Double down on important rules and best practices.
Common Prompting Use Cases
Extracting Data
This includes tasks like extracting URLs from search results or data points from a LinkedIn profile. LMs are good at this, so you can often get away with short structured prompting and cheaper models like GPT-3.5.
Classification or Categorization
For simple classifications like "action needed" or "no action needed," short structured prompting works well. For more subjective classifications, you may need a long structured prompt and a better model.
Generating Content
This is the hardest task for LMs and requires long structured prompting and the best models like GPT-4 or GPT-4.5.
Evaluation
Tasks like evaluating the relevance of search results or sentiment analysis require long structured prompts and the best models.
Data Transformation
For tasks like reformatting a CV or converting data to JSON, you can often use short structured prompting. However, for more complex transformations, a long structured prompt and a better model are recommended.
Decision-Making
This is where AI agents come in. Always use the best models and the agent prompting framework for these tasks.
Special Prompting Techniques in Relevance AI
Tool Input Prompting
Name and describe tools in a way that makes it easy for the agent to understand what the tool does.
Sub-Agent Prompting
Clearly describe what each sub-agent does and how to communicate with them.
Labeling and Naming Tasks
Use labels and names to organize tasks better. However, be cautious as this can sometimes lead to errors.
Flow Builder
Use the flow builder for complex SOPs to make the process clearer and more reliable.
Conclusion
Good prompt engineering is the cornerstone of building reliable AI systems. By following these frameworks and best practices, you can significantly improve the reliability, accuracy, and cost-efficiency of your AI agents and automation systems.
FAQs
How do I choose the right framework for my task?
It depends on the complexity of the task. For simple tasks, use the short structured framework. For complex tasks, use the long structured framework. For agents, use the agent prompting framework.
Can I use cheaper models for all tasks?
No, the choice of model depends on the task. For simple data extraction, cheaper models like GPT-3.5 work well. For complex tasks like content generation, use the best models like GPT-4 or GPT-4.5.
What is chain prompting?
Chain prompting involves breaking down a complex task into smaller tasks and using the output of one task as the input for the next. This increases the reliability and consistency of your system.
How do I handle edge cases in prompts?
Include examples of edge cases in the examples section of your prompt. This helps the LM understand how to handle these cases better.
What is markdown formatting and why is it important?
Markdown formatting helps structure your prompt with headers, bold text, and bullet points. It makes the prompt easier for the LM to understand and follow.
By mastering these techniques, you'll be well on your way to building robust and reliable AI systems. Happy prompting!
