DAY 1: Understanding AI, Data Privacy, and Setting Up GPT Models

Module 1: Introduction to Generative AI and GPT Models

Module 2: Data Privacy, Compliance, and Preparing Data for AI

Overview of key banking data privacy regulations: PDPA, GDPR, and financial data laws.
Best practices for ensuring compliance and safeguarding sensitive customer data during model training.
Preparing and structuring data for uploading into the GPT model.
Setting up model parameters: Token limits, response variability, and knowledge scope.
Understanding domain-specific fine-tuning for financial products and selecting training data sources.

Module 3: Prompt Engineering for GPT Models

Introduction to the concept of prompt engineering.
An overview of what prompts are and how they guide the output of ChatGPT. Explanation of key principles in creating effective prompts, including clarity, specificity, and tone.
Understanding the structure of prompts and how they influence GPT model responses.
Exploration of a few techniques for prompting, including the use of system instructions (guiding the AI's behavior with specific instructions), and role-playing methods (pretending the AI is a specific character or professional).

Module 4: Customer Segmentation and GPT in the Digital Marketing Funnel

Introduction to the AIDA (Awareness, Interest, Desire, Action) model in digital marketing.
Using GPT models to segment customers based on uploaded data.
Applying GPT across each stage of the marketing funnel:
Awareness: Crafting content to introduce financial products.
Interest: Generating personalized insights to engage customers.
Desire: Customizing offers based on customer profiles and purchase history.
Action: Creating scripts and automated responses to close sales.
Best practices for designing complex prompts to handle objections and specific customer inquiries.

Day 2: Simulating Customer Engagement and Ensuring Model Accuracy

Module 5: Sales Script Generation for Conversion

Developing personalized sales scripts for various customer segments using GPT-generated insights.
Crafting customized scripts for online, phone, and in-person customer interactions.
Aligning scripts with the "Desire" and "Action" stages of the digital marketing funnel for better conversion rates.
Best practices for fine-tuning prompt strategies for different stages of the digital marketing funnel (Awareness, Interest, Desire, Action).

Module 6: Failsafe Mechanisms for GPT Models

Failsafe Workflow: Teaching GPT models to recognize when they are operating outside their knowledge base and how to respond to extreme or out-of-scope scenarios.
Importance of designing workflows that handle unpredictable customer inputs and ensure compliance.
Building robust failover responses for out-of-bounds customer queries.

Module 7: Simulating Customer Interactions and Handling Objections

Simulating real-world customer conversations using GPT.
Training the model to handle customer objections and navigate sales negotiations.
Creating scripts to respond to typical customer concerns, such as price objections or product comparisons.

Module 8: Evaluating Training Data Quality and Ensuring Model Accuracy

Importance of both quantity and quality of data in GPT training.
Methods for critically evaluating the training data sources for better accuracy and reliability of GPT outputs.
Reviewing how training data impacts the accuracy and reliability of customer segmentation, interactions, and sales conversions.

GAI-CL1: Enhancing the Advisory Role in Financial Sales with Generative AI