LlamaIndex: Developing LLM Powered Applications Training Course
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.
Course Outline
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
Requirements
- 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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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