I Was Prompting AI Agents Wrong... Until I Learned This (Live Prompting Examples)

Summary

This video provides an in-depth guide on building reactive AI agents with tools like Google Calendar and email management. It highlights the iterative process of setting up agents, testing them, and refining their behavior through reactive prompting. The tutorial focuses on integrating tools, handling errors, and providing detailed instructions for specific tasks such as creating events and labeling emails. The process emphasizes trial-and-error adjustments, ensuring the agent's output is accurate and efficient. The video is aimed at anyone looking to optimize AI agent development with a practical, test-heavy approach.

Takeaways

• 😀 The process of building AI agents involves defining sections such as instructions, tools, output formatting, and final notes to guide their behavior.
• 😀 Reactive prompting is essential to adjust an agent's behavior in real-time, especially when errors arise during testing.
• 😀 Clear output formatting is critical, and in some cases, specific instructions (e.g., HTML formatting, links, images) must be included in the prompt to ensure the desired structure.
• 😀 Tools can be added to an AI agent dynamically, and it's crucial to ensure they are correctly integrated into the system prompt for the agent to use them effectively.
• 😀 Testing the agent’s functionality is an iterative process of 'build, test, change,' where adjustments are made based on issues encountered during testing.
• 😀 Even small details in the prompt, such as specifying the current date and time, can impact how an agent handles tasks like event creation or email scheduling.
• 😀 Memory and error handling should be considered, but they may not always require separate sections depending on the complexity of the agent.
• 😀 The use of descriptive tool names and actions (e.g., 'create event', 'send email') helps the agent understand how to perform the desired tasks.
• 😀 When adding new tools (e.g., Gmail tools for labeling emails), it’s important to ensure the agent understands the correct sequence of actions required for successful execution.
• 😀 Iterative refinement is key: if an agent's tool interaction isn't working as expected, breaking down the steps (e.g., get emails first, then get labels) can resolve issues and lead to a more efficient workflow.

Q & A

What are the key sections involved in structuring an AI agent prompt?

The key sections for structuring an AI agent prompt include the Instructions, Tools, Output, and Final Notes sections. The Instructions guide the agent's behavior, the Tools section defines specific tasks or tools available, the Output section dictates the format of the responses, and the Final Notes section includes miscellaneous reminders like current date, time, and formatting rules.

Why is it important to have a Final Notes section in the prompt?

The Final Notes section is crucial because it ensures important miscellaneous information is not overlooked. It can include reminders like the current date, specific formatting instructions, or instructions on handling missing information, ensuring the agent remains consistent and accurate throughout the task.

How do you manage output formatting in an AI agent?

Output formatting is managed by providing detailed instructions on how the AI should structure its response. For example, in the case of a content creator agent, instructions can specify the use of HTML formatting, headers, horizontal lines, clickable links, and other formatting elements to ensure the response matches specific requirements.

What role does reactive prompting play in AI agent development?

Reactive prompting is used to adjust the AI agent’s behavior after observing errors or inconsistencies during task execution. It involves making adjustments to the prompt to correct any issues, such as providing missing context (like the current date) or fixing formatting errors, ensuring the agent performs as expected.

Can you give an example of reactive prompting in action?

An example of reactive prompting is when the AI agent created a calendar event but used an incorrect date. The prompt was updated with the correct current date and time in the Final Notes section, ensuring that future event creation tasks would be accurate.

What is the purpose of having a Tools section in the prompt?

The Tools section defines which specific tools the AI agent can use for its tasks. It ensures the agent understands how to access and use these tools correctly, helping it perform actions like creating events, sending emails, or labeling emails.

How does the AI agent know when to use each tool during its operation?

The AI agent determines when to use a tool based on the instructions and tool descriptions provided in the prompt. For example, the agent might be instructed to first retrieve emails before adding labels, ensuring the correct sequence of actions.

What is the importance of error handling in AI agent development?

Error handling is important because it allows the agent to handle unexpected situations or failures in a graceful manner. By incorporating error handling into the prompt, you can ensure the agent doesn't get stuck or confused when things go wrong, and can attempt to fix the issue or provide a suitable response.

How can memory management be incorporated into an AI agent?

Memory management can be included in the Rules or Instructions section of the prompt, where the agent is informed about what information it should remember, how to use past context, and when to reset or forget previous data. This helps the agent maintain consistency across multiple interactions or tasks.

What is the iterative process in AI agent development, and why is it important?

The iterative process in AI agent development involves building, testing, identifying issues, and making adjustments based on test results. This cycle is crucial because it helps refine the agent's performance, ensuring that errors are fixed and the agent continuously improves over time.