LlamaIndex: Developing LLM Powered Applications

• An understanding of Python programming and basic machine learning concepts
• Experience with APIs and application development
• Familiarity with natural language processing is beneficial but not required

Audience

• Developers
• Data scientists

LlamaIndex is a powerful indexing tool designed to enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve and utilize custom data sets effectively.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level developers and data scientists who wish to master LlamaIndex for developing innovative LLM-powered applications.

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

• Set up and configure LlamaIndex for use with LLMs.
• Index and query custom datasets using LlamaIndex to enhance LLM functionality.
• Design and develop sophisticated applications that utilize LlamaIndex and LLMs.
• Understand and apply best practices for working with LLMs and LlamaIndex.
• Navigate the ethical considerations involved in deploying LLM-powered applications.

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

• Understanding LlamaIndex and its role in LLMs
• Setting up LlamaIndex: environment and prerequisites
• The basics of indexing custom data

LlamaIndex in Action

• Querying with LlamaIndex: techniques and best practices
• Building query and chat engines with LlamaIndex
• Creating intuitive Streamlit interfaces for LLM applications

Advanced LlamaIndex Features

• Employing retrieval-augmented generation (RAG) for enhanced data retrieval
• Leveraging vectorstores for efficient data management
• Designing and implementing LlamaIndex agents

Application Development with LlamaIndex

• Prompt engineering: chain of thought, ReAct, few-shot prompting
• Developing a documentation helper: a real-world LLM application
• Debugging and testing LLM applications

Deployment and Scaling

• Deploying LlamaIndex-based applications
• Scaling LLM applications for high performance
• Monitoring and optimizing LLM applications

Ethical and Practical Considerations

• Navigating ethical implications in LLM applications
• Ensuring privacy and data security with LlamaIndex
• Preparing for future developments in LLM technology

Summary and Next Steps