Prompting for Effective LLM Reasoning and Planning

Overview

Go beyond basic chatbots and learn to engineer sophisticated AI agents. Learn advanced prompting techniques that power modern AI. You'll master Chain-of-Thought, ReAct, and feedback loops to build systems that can reason, plan, and solve complex problems. Through hands-on exercises, you will transform generic AI into specialized, reliable tools, culminating in building a multi-agent travel planner from scratch.

Syllabus

Introduction to Prompting for Effective LLM Reasoning and Planning

Introduces the core concepts of Agentic AI, the course structure, prerequisites, and learning environment.

The Role of Prompting in Agentic AI with Python and OpenAI

Learn what AI Agents are and how they work. Understand the critical role prompting plays in guiding them to reason, plan, and act to achieve goals.

Role-Based Prompting

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Implementing Role-Based Prompting with Python

Provides hands-on practice in iteratively developing a role-based prompt to create a believable historical figure persona.

Chain-of-Thought and ReACT Prompting

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Applying COT and ReACT Prompting with Python

Provides hands-on practice implementing both CoT and ReAct prompts to solve a retail analytics problem.

Prompt Instruction Refinement

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Applying Prompt Instruction Refinement with Python

Provides hands-on practice iteratively refining a prompt to transform a generic recipe analyzer into a precise dietary consultant that produces structured JSON.

Chaining Prompts for Agentic Reasoning

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Chaining Prompts with Python

Provides hands-on practice implementing a three-stage prompt chain with Pydantic-based gate checks to automate an insurance claim triage process.

LLM Feedback Loops

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

Implementing LLM Feedback Loops with Python

Provides hands-on practice building an automated feedback loop where an AI generates Python code, has it tested against a unit test suite, and uses the test results as feedback to debug itself.