LlamaIndex: Developing LLM Powered Applications Training Course
LlamaIndex is a robust indexing framework designed to boost the capabilities of Large Language Models (LLMs) by enabling them to efficiently retrieve and leverage custom datasets.
This instructor-led, live training (available online or onsite) targets intermediate to advanced developers and data scientists aiming to master LlamaIndex for creating innovative applications driven by LLMs.
Upon completion of this training, participants will be able to:
- Install and configure LlamaIndex for integration with LLMs.
- Index and query custom datasets using LlamaIndex to improve LLM performance.
- Architect and build sophisticated applications that harness both LlamaIndex and LLMs.
- Comprehend and implement best practices for working with LLMs and LlamaIndex.
- Address the ethical considerations associated with deploying LLM-powered solutions.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For inquiries regarding customized training for this course, please contact us to make arrangements.
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Duration 42 hours
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
- A solid understanding of Python programming and fundamental machine learning concepts
- Experience with APIs and application development
- Familiarity with natural language processing is advantageous but not mandatory
Target Audience
- Developers
- Data scientists
Open Training Courses require 5+ participants.
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