Building Accurate Prompts for Enterprise Chatbots Using Financial Datasets

Overview

Master prompt engineering techniques for enterprise chatbots, focusing on financial datasets and CLOs. Learn iterative refinement, performance evaluation, and optimization strategies for creating precise LLM outputs.

Syllabus

• Introduction to Prompt Engineering for Enterprise Chatbots
Overview of Large Language Models (LLMs)
Importance of Prompt Engineering in Enterprise Contexts
Key Challenges in Financial Domain
• Understanding Financial Datasets
Types of Financial Datasets (CLOs, market data, etc.)
Data Privacy and Security Considerations
Preparing and Cleaning Financial Data for LLMs
• Designing Effective Prompts
Structure of a Good Prompt
Crafting Questions for Financial Contexts
Contextual Dependencies and Relevance
• Iterative Prompt Refinement
Techniques for Refining Prompts
Identifying and Addressing Ambiguities
Use of User Feedback for Iterative Improvement
• Performance Evaluation of Chatbot Outputs
Metrics for Evaluating LLM Responses
Common Performance Pitfalls in Financial Chatbots
Tools and Frameworks for Automated Evaluation
• Optimization Strategies for Prompt Engineering
Balancing Precision and Recall
Leveraging Contextual Clues from Financial Data
Real-World Case Studies and Best Practices
• Ethical Considerations in AI and Financial Services
Bias in Financial Chatbot Interactions
Ensuring Transparency and Fairness
Regulatory Compliance in AI Interactions
• Future Trends in Enterprise Chatbots and AI
Emerging Technologies and Their Impact
The Role of AI in Financial Decision-Making
Preparing for Evolving Challenges in Prompt Design
• Conclusion and Future Learning Paths
Recap of Key Learning Outcomes
Resources for Further Learning
Opportunities for Hands-On Projects and Experiments

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Subjects

Data Science