Enhance LLMs using RAG and Hugging Face
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Discover how to enhance large language models (LLMs) using Retrieval-Augmented Generation (RAG) with Hugging Face. Learn how to incorporate external data into LLMs for more accurate and contextually relevant outputs.
At a Glance
Learn Retrieval-Augmented Generation (RAG) by building context-aware Large Language Models (LLMs). This guided project leverages Hugging Face and Facebook AI Similarity Search (FAISS) for efficient semantic search and natural language generation, enabling personalized, context-rich responses from your own documents. Ideal for advancing your understanding of AI techniques and enhancing the capabilities of LLMs with relevant contextual information.
A look at the project ahead
- Understand and implement the Retrieval-Augmented Generation (RAG) framework, integrating Hugging Face’s BART and DPR models for robust document retrieval and response generation.
- Gain hands-on experience in using FAISS for efficient indexing and retrieval, enabling scalable and fast semantic search within your custom document collection.
What you’ll need
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