Overview

This course equips you with essential skills to harness Large Language Models (LLMs) in the finance sector. It covers foundational concepts including role-based prompting, chain-of-thought, and ReACT prompting, complemented with practical Python implementations tailored for financial applications. You will explore techniques for refining prompt instructions and chaining prompts to enable agentic reasoning. The course also emphasizes the integration of feedback loops to enhance LLM performance. The final project is a comprehensive transactional risk analysis and compliance engine. By the end, you will be adept at deploying LLMs for strategic financial decision-making.

Syllabus

Introduction to 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.

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 for Financial Services

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

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 for Financial Services

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

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 for Financial Services

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

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 and OpenAI for Financial Services

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.

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 for Financial Services

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