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Retrieval-Augmented Generation (RAG) with Embedding-Based Dense Retrieval

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RAG is a technique where a generative AI model (like ChatGPT) doesn’t just rely on its own training data to generate responses. Instead, it retrieves relevant information from external sources (like databases or documents) to provide more accurate and up-to-date answers.

2. Keyword-Based Retrieval

Keyword-based retrieval is the traditional method used to find relevant information. Here’s how it works:

  • Keywords Extraction: The system looks for specific words or phrases (keywords) that match the user’s query.
  • Matching: It searches the external documents for those exact keywords.
  • Retrieval: Documents containing those keywords are retrieved and used to generate the response.

Example:

  • User Query: "Best restaurants in New York"
  • Keywords Extracted: "best," "restaurants," "New York"
  • Process: The system finds documents that contain these words to provide a list of top restaurants in NYC.

Pros:

  • Simple and fast.
  • Easy to implement.

Cons:

  • Doesn’t understand the context or meaning behind the words.
  • Can miss relevant information if different words are used.
  • Less effective with complex queries.

3. Embedding-Based Dense Retrieval

Embedding-based dense retrieval uses advanced techniques to understand the meaning and context of the query and documents. Here’s how it works:

  • Embeddings Creation: Both the user’s query and the documents are converted into high-dimensional vectors (embeddings) that capture their meanings.
  • Semantic Matching: Instead of just matching exact words, the system compares these vectors to find documents that are semantically similar to the query.
  • Retrieval: The most relevant documents based on this semantic similarity are retrieved.

Example:

  • User Query: "Top eateries in NYC"
  • Embedding Process: The system understands that "eateries" is similar to "restaurants" and "NYC" is the same as "New York City."
  • Process: It retrieves documents related to the best places to eat in New York City, even if they use different wording.

Pros:

  • Understands context and meaning, not just exact words.
  • Finds more relevant information, even with different phrasing.
  • Better handles complex and nuanced queries.

Cons:

  • More computationally intensive.
  • Requires sophisticated models to create and compare embeddings.

4. Why the Shift from Keyword-Based to Embedding-Based?

  • Improved Accuracy: Embedding-based retrieval provides more relevant and contextually accurate results.
  • Better User Experience: Users get answers that better match their intent, even if they phrase things differently.
  • Handling Complexity: It’s more effective for complex queries that involve understanding relationships and meanings beyond simple keywords.

In Summary

  • Keyword-Based Retrieval: Looks for exact word matches. Simple but can miss the bigger picture.
  • Embedding-Based Dense Retrieval: Understands the meaning and context, finding relevant information even with different wording. More powerful but requires advanced technology.

In the realm of GenAI RAG, moving from keyword-based to embedding-based dense retrieval means AI models can provide more accurate, relevant, and context-aware responses by better understanding and retrieving the information that truly matches the user's intent.

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