Prompt Engineering for Agentic Workflows
Chapter 1: Foundations of Agentic AI Systems
Defining Agentic Workflows
Core Components of AI Agents
The Function of Prompt Engineering in Agentic Systems
Contrasting Agent Prompts with Standard Prompts
Overview of Agent Architectures
Methods for Assessing Agent Performance
Hands-on: Analyzing a Simple Agent's Task Execution
Quiz for Chapter 1
Chapter 2: Advanced Prompting for Agent Control
Structuring Prompts for Sequential Operations
Refining Instructions for Complex Agent Actions
Assigning Roles and Personas to Agents
Utilizing Few-Shot Examples for Agent Guidance
Chain-of-Thought and Tree-of-Thought in Agent Prompts
Controlling Agent State via Prompt Design
Prompting for Self-Correction and Error Handling
Practice: Crafting Prompts for Coherent Agent Behavior
Quiz for Chapter 2
Chapter 3: Prompt Engineering for Tool Use
Principles of Tool-Augmented Language Models
Prompting Agents for Tool Selection and Operation
Formatting Tool Specifications for LLM Comprehension
Managing Tool Inputs and Outputs through Prompts
Prompts for Interacting with APIs
Addressing Errors During Tool Execution via Prompts
Designing Prompts for Novel Tool Integration
Hands-on: Guiding an Agent to Use a Web Search Tool
Quiz for Chapter 3
Chapter 4: Prompts for Agent Planning and Task Management
Introduction to AI Planning in Agent Systems
Prompting for Goal Specification and Refinement
Breaking Down Complex Problems using Prompts
Incorporating Constraints and Preferences in Agent Plans
Iterative Planning and Re-planning with Prompt Adjustments
Prompt Strategies for Hierarchical Task Decomposition
Evaluating Plan Viability and Quality
Practice: Prompting an Agent to Formulate a Detailed Plan
Quiz for Chapter 4
Chapter 5: Managing Agent Memory with Prompts
The Significance of Memory in Agent Operations
Prompt Strategies for Short-Term Memory and Context Windows
Information Condensation Techniques for Prompts
Prompting for Long-Term Information Retrieval
Structuring Prompts for Accessing Knowledge Stores
Managing Information Consistency and Beliefs
Maintaining Task Focus Across Extended Interactions
Hands-on: Prompting an Agent with Dynamic Information Streams
Quiz for Chapter 5
Chapter 6: Debugging and Optimizing Prompts in Agentic Systems
Common Issues in Agent Prompt Implementations
A Systematic Approach to Prompt Iteration and Testing
Effects of Prompt Chaining on Agent Outputs
Methods for Analyzing Agent Action Sequences
Comparing Prompt Variations for Agent Effectiveness
Logging and Monitoring for Prompt Improvement
Organizing and Versioning Prompts for Agents
Hands-on: Refining Prompts to Resolve an Agent Workflow Issue
Quiz for Chapter 6
Prompting for Self-Correction and Error Handling
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- ReAct: Synergizing Reasoning and Acting in Language Models, Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao, 2022 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2210.03629 - Introduces the ReAct paradigm, a foundational approach for LLM agents combining reasoning and acting, including handling tool interactions and their potential outcomes. This approach is valuable for graceful error recovery in agentic workflows.
- Reflexion: Language Agents with Verbal Reinforcement Learning, Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023 arXiv preprint arXiv:2303.11366 DOI: 10.48550/arXiv.2303.11366 - Presents a framework for LLM agents to perform verbal reinforcement learning by reflecting on past action sequences and generating self-reflection prompts to guide future actions. This is directly relevant to prompting for self-critique and iterative modification upon failure.
- Constitutional AI: Harmlessness from AI Feedback, Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Anna Chen, Andy Jones, Anna Goldieberger, Aziza Mirrashed, Cameron McKinnon, Carol Chen, Catherine Olsson, Chris Conly, David Drain, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jackson Kernion, Jasmine Sittler, Jennifer Glowinski, Jeremy Scheurer, Jessica Kerr, Josh Jacobson, Kristen Lee, Liane Lovitt, Lisa Wang, Michael Sellitto, Mo Mukherjee, Nicholas Joseph, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Shauna Gordon-McKeon, Simon Lefevre, Tristan Hume, Zac Hatfield-Dodds, Danny Hernandez, Daniela Amodei, Dario Amodei, Jack Clark, Sam McCandlish, Tom Brown, Jared Kaplan, 2022 arXiv preprint arXiv:2209.07858 DOI: 10.48550/arXiv.2209.07858 - While primarily focused on safety, this paper introduces a method where AI models critique and revise their own outputs based on a set of principles, showing a robust form of AI-driven self-assessment and correction.