Prompting for Effective LLM Reasoning and Planning

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

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.

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

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

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

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

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

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

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.

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

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

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.

Course review