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RAG

Learn about RAG and how it counters the limitations of LLMs.

What is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) combines information retrieval techniques with Large Language Models (LLMs) to generate highly accurate, contextually enriched responses. By first retrieving relevant external information, RAG ensures the outputs produced by language models are both precise and grounded in up-to-date knowledge.

Why RAG Matters

Large Language Models, despite their generative strengths, come with limitations:

  • Knowledge Gaps: Their training is static, unable to incorporate information beyond a specific cutoff date.
  • Risk of Hallucinations: They can generate content that sounds plausible but is incorrect.
  • Limited Domain Expertise: Generic LLMs often lack detailed knowledge about specialized or niche topics.

RAG directly addresses these issues by augmenting language models with timely, relevant, and precise context drawn from trusted sources.

How RAG Works

Upon receiving a query, the system immediately retrieves relevant chunks from a specific knowledge base such as internal documentation, technical manuals, or recent publications. This retrieval leverages:

  • Embeddings: Converting both query and documents into embeddings that capture semantic meaning.
  • Vector Search: Quickly identifying the most semantically relevant embeddings from a large dataset.
  • Reranking: Refining these initial retrieval results, ensuring only the most relevant chunks proceed to the generation stage.

The selected information chunks are integrated with the original query into a structured prompt. This combined input is then fed into an LLM, enabling the model to generate a contextually accurate, detailed, and relevant response.

Benefits of Using RAG

  • Enhanced Accuracy: Reduces errors and misinformation by ensuring generated responses are always grounded in verifiable sources.
  • Real-Time Knowledge: Enables language models to access and utilize current, dynamic information beyond their static training data.
  • Domain-Specific Insights: Empowers models with the ability to provide expert-level knowledge tailored to specific fields or internal datasets.
  • Transparent Outputs: Provides traceability by revealing the exact sources used to generate each response, enhancing credibility and user trust.

By effectively combining embeddings, vector search, and reranking, RAG delivers consistently reliable, informative, and trustworthy outputs, ensuring your LLMs perform at their highest potential.

Last updated: May 2, 2025