Duration
3 days
Course Overview
This comprehensive 3-day course is designed to guide bank employees through the process of developing an advanced Autobot for a Loan Origination System. Leveraging the power of Large Language Models (LLMs), participants will learn to create an AI-powered assistant capable of handling customer inquiries in both written and audio formats. The course combines theoretical knowledge with extensive hands-on practice, enabling participants to build, deploy, and integrate a sophisticated Autobot that enhances the efficiency and user experience of the bank's loan origination process.
Course Objectives
By the end of this course, participants will be able to:
- Understand the principles of conversational AI and its applications in banking, particularly in loan origination
- Grasp the fundamentals of Large Language Models, with a focus on their application in question-answering systems
- Preprocess and structure FAQ data for optimal use in an AI-powered system
- Fine-tune a GPT model for domain-specific question answering in the context of loan origination
- Develop a functional Autobot capable of handling customer inquiries about loan origination
- Implement advanced features such as context awareness and follow-up question handling
- Integrate text-to-speech and speech-to-text capabilities into the Autobot
- Connect the Autobot to existing banking systems while ensuring data security and regulatory compliance
- Evaluate and optimize the performance of the Autobot using relevant metrics and user feedback
Day 1: Foundations and FAQ Processing
Morning Session
- 1. Introduction to conversational AI and its applications in banking
- - Overview of chatbots and virtual assistants
- - Importance of AI in loan origination systems
- 2. Understanding Language Models for Q&A systems
- - Brief overview of different LLMs (BERT, GPT, T5)
- - Deep dive into GPT for question-answering tasks
Afternoon Session
- 3. Hands-on: Setting up the development environment
- - Installing necessary libraries (PyTorch, Transformers)
- - Introduction to Hugging Face Transformers library
- 4. Processing and structuring FAQ data
- - Techniques for organizing and formatting FAQ content
- - Hands-on exercise: Preprocessing the bank's existing FAQ database
Day 2: Model Development and Implementation
Morning Session
- 5. Fine-tuning GPT for FAQ answering
- - Understanding transfer learning and fine-tuning concepts
- - Hands-on: Fine-tuning GPT on the bank's FAQ dataset
Afternoon Session
- 6. Implementing the Autobot
- - Designing the Autobot architecture
- - Hands-on: Building the Autobot using the fine-tuned model
- 7. Handling user queries and generating responses
- - Implementing query processing and response generation
- - Techniques for improving response relevance and coherence
Day 3: Advanced Features and Integration
Morning Session
- 8. Implementing text-to-speech and speech-to-text
- - Introduction to speech processing libraries
- - Hands-on: Integrating audio capabilities into the Autobot
- 9. Enhancing Autobot with context awareness
- - Techniques for maintaining conversation context
- - Implementing follow-up question handling
Afternoon Session
- 10. Integration with Loan Origination System
- - Strategies for connecting Autobot to existing banking systems
- - Handling sensitive information and ensuring data security
- 11. Testing and evaluation of the Autobot
- - Metrics for assessing Autobot performance
- - A/B testing and user feedback collection methods
- 12. Deployment considerations and best practices
- - Model serving options for Autobot
- - Addressing regulatory and compliance concerns in AI banking applications
- 13. Wrap-up and Q&A session
- - Recap of key learnings
- - Discussion on potential extensions and improvements
Programme scope, delivery arrangements and suitability are confirmed during enquiry.