Prompting for LLM Reasoning and Planning for Financial Services

Explore the basics of LLM reasoning and planning, with a focus on applications in financial services, guided by industry expert.

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

Transform generic AI into expert financial advisors by iteratively refining prompts to define role, expertise, and communication style for tailored, actionable client advice.

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

Learn to enhance financial fraud detection with Python by applying Chain-of-Thought and ReACT prompting for systematic, transparent, and tool-integrated LLM reasoning.

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

Learn to refine AI prompts with Role, Task, Output Format, Examples, and Context using Python, generating expert, actionable financial analysis for client-ready reports.

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.

Learn to build robust, auditable financial AI workflows by chaining specialized prompts with Python and OpenAI, using Pydantic models as validation gates for data integrity at each stage.

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

Learn to implement Python-based LLM feedback loops in finance, enabling multi-agent collaboration for quality-controlled, compliant investment recommendations through iterative refinement.