LlamaIndex: Enhancing Contextual AI

• Basic understanding of AI and machine learning concepts
• Familiarity with Large Language Models (LLMs)
• Experience with programming and data handling

Audience

• AI researchers
• Machine learning professionals
• Data scientists

LlamaIndex is an open-source data framework designed for applications that use Large Language Models (LLMs) and benefit from context augmentation. It's particularly useful for systems known as Retrieval-Augmented Generation (RAG) systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI researchers, machine learning professionals, and data scientists who wish to use LlamaIndex to enhance the capabilities of AI models, making them more accurate and reliable for various applications.

By the end of this training, participants will be able to:

• Understand the principles and components of LlamaIndex.
• Ingest and structure data for use with LLMs.
• Implement context augmentation to improve AI model performance.
• Integrate LlamaIndex into existing AI systems and workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to LlamaIndex and Context Augmentation

• Overview of LlamaIndex
• The role of context augmentation in AI
• Benefits of using LlamaIndex with LLMs

Setting Up LlamaIndex

• Installation and configuration
• Understanding the architecture and components
• Data connectors and ingestion

Data Indexing and Access

• Creating data indexes for efficient access
• Query engines and natural language access
• Best practices for data structuring

Integrating LlamaIndex with LLMs

• Enhancing LLMs with contextually relevant data
• Practical exercises: Augmenting chatbots and text generators
• Troubleshooting and optimization

Application Scenarios and Case Studies

• Use cases in various industries
• Review of successful implementations
• Building a context-augmented AI solution

Summary and Next Steps