Retrieval Augmented Generation: Making LLMs Context-Aware

Ever wondered how ChatGPT plugins work? Or how companies use LLMs with their private data? This workshop dives into Retrieval Augmented Generation (RAG), the technique powering context-aware AI applications.

Prerequisites

• Basic understanding of LLMs and transformers
• Familiarity with embeddings and vector databases
• Basic Python programming knowledge
• Previous workshops in the series recommended but not required

What You’ll Learn

• Core RAG Components:

• Document processing and chunking strategies

• Vector stores and efficient retrieval

• Prompt engineering for RAG

• Practical Implementation:

• Building a RAG pipeline from scratch

• Integration with popular vector databases

• Performance optimization

• Evaluation metrics and testing

By Workshop’s End

You’ll gain the ability to:

• Build end-to-end RAG systems
• Choose appropriate retrieval strategies
• Optimize RAG performance
• Deploy production-ready RAG applications

Ready to make LLMs context-aware? Workshop Link