Registration (8.00 – 8.30)
Welcome and overview (8.30 – 9.00)
- Introduction to AI, LLMs, and their relevance to Delphi applications.
- Course objectives and structure.
- Envisioning the future: exploring potential AI-driven solutions in Delphi applications.
Module 1: Understanding LLMs and embeddings (9.00 – 10.00)
- Quick overview of LLMs, embeddings, and their use cases.
- Hands-on: Setting up a Delphi project with API integration for embeddings.
Module 2: Connecting to AI APIs with Delphi (10.00 – 11.00)
- Making REST API calls to access LLM services.
- Handling responses and integrating output into Delphi applications.
- Hands-on: Building a chatbot in Delphi using LLM APIs and custom data.
Break (15 minutes)
Module 3: Introduction to Vector Databases (11.15 – 12.30)
- Overview of vector databases and their role in AI-driven applications
- Integrating vector databases into Delphi applications for efficient data retrieval.
- Hands-on demo: Connecting and querying a vector database.
Lunch Break (1 Hour)
Module 4: Advanced application with Retrieval-Augmented Generation (13.30 – 14.30)
- What is a RAG, and how it improves LLM responses.
- Hands-on: Creating an intelligent assistant using RAG in Delphi.
Module 5: Agents and their applications (14.30 – 15.30)
- What are agents? Automating workflows and tasks using LLMs and agents.
- Integrating simple agent behaviors in Delphi applications.
- Hands-on: Building a basic agent-driven application using an LLM.
Break (15 minutes)
Module 6: Fine-Tuning LLMs (15.45 – 16.45)
- Overview of fine-tuning: why and how it works.
- Preparing a dataset for fine-tuning.
- Hands-on: Walkthrough of fine-tuning a small LLM and integrating the model output into Delphi.
Module 7: Code generation (16.45 – 17.15)
- Working with your source code to analyse, find bugs and generate new code.
- Use smart solutions to help you code faster and better.
Wrap-up and Q&A (17.15 – 17.30)
- Review of what you have learned
- Discussion on applicability in your own environment
- Q&A
