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Key difference between RAG and embedding

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The key difference between RAG (Retrieval-Augmented Generation) and embeddings is:

  • RAG is a technique that combines large language models (LLMs) with information retrieval systems to generate responses. It uses embeddings as part of the retrieval process, but RAG encompasses the overall architecture and workflow.

  • Embeddings are dense vector representations of text that capture semantic meaning and relationships. They are a crucial component in RAG, enabling the retrieval of relevant information to augment the LLM’s generation.

Here are some more details on their relationship:

Role of Embeddings in RAG

  • Embeddings are used to encode both the user query and the available information (e.g. documents, passages) in a shared semantic space.
  • This allows the RAG system to efficiently retrieve the most relevant information by finding the chunks with embeddings closest to the query embedding.
  • The quality of the embeddings directly impacts the retrieval performance and ultimately the quality of the generated responses.

RAG Architecture

  • RAG combines an LLM for generation with a retrieval system that uses embeddings to find relevant information.
  • The retrieved information is then used to augment the LLM’s input, providing additional context to improve the generated output.
  • The retrieval system can use various techniques like nearest neighbor search over embeddings to efficiently find the most relevant information.

Embedding Models

  • Embeddings are produced by specialized models like BERT, GPT, or custom models trained for the specific task.
  • The choice of embedding model is crucial and can significantly impact RAG performance. Factors like model size, training data, and architecture affect the quality of the embeddings.

In summary, while embeddings are a core component, RAG is a broader concept that leverages embeddings as part of a retrieval-augmented generation workflow to enhance the capabilities of large language models. The quality of embeddings is critical for effective retrieval in RAG systems.

AI, LLM
rag embedding basics
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