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How RAG Empowers Accurate Gen AI
How RAG Empowers Accurate Gen AI

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RAG, which stands for Retrieval-Augmented Generation, is a technique that improves the accuracy and reliability of large language models (LLMs). RAG tackles the issue of hallucinations in LLMs by providing factual grounding to the generation process.

The ways in which RAG works are:

 

 

 

 

 

 

 

 

 

 

 

1. Identifying Relevant Information: When a user asks a question or prompts the LLM, RAG doesn't rely solely on the LLM's internal knowledge. Instead, it actively searches through external data sources (e.g., company databases, knowledge graphs) for information relevant to the specific query.

2. Enhanced Prompt Creation: Based on the retrieved information, RAG creates an "enhanced prompt" that combines the user's original query with relevant facts and data points. This enriched prompt provides the LLM with a more grounded foundation for generating its response.

3. Improved Accuracy: By anchoring the generation process in real-world data, RAG helps the LLM produce more accurate and reliable outputs. Less reliance on internal knowledge, which might contain biases or inaccuracies, reduces the chances of the LLM hallucinating or fabricating information.

RAG acts as a fact-checker for the LLM, reducing hallucinations by providing a strong foundation in factual data for generating responses. It is a significant development in the field of Gen AI, paving the way for more reliable and trustworthy applications of LLMs.

Sources:

  1. Make room for RAG: How Gen AI's balance of power is shifting
  2. Why Enterprises Like to RAG

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