RAG

RAG is a technique used to improve the accuracy and reliability of large language models (LLMs).

RAG allows language models to be applied to a wide range of tasks by providing access to relevant knowledge bases. Retrieval-augmented generation (RAG) is an architectural approach that enhances the capabilities of large language models (LLMs) by leveraging external data sources. 

Purpose and functionality: RAG improves the efficacy of LLM applications by retrieving relevant data/documents and providing them as context to the LLM. It allows LLMs to access up-to-date information and domain-specific knowledge beyond their initial training data.

How RAG works: RAG retrieves relevant information from external sources (databases) when a query is issued. This retrieved information is then integrated into the LLM's input, allowing it to generate more accurate and contextually relevant responses.

Goals of RAG:

-Improve accuracy and reliability of LLM outputs.

-Reduce hallucinations (inaccurate or fabricated information) by providing factual context.

-Enable access to current and domain-specific information without retraining the model.

-Allow citation of sources, building user trust.

-More cost-effective than fine-tuning or retraining models.

Common use cases of RAG:

-Customer support chatbots with company-specific knowledge.

-Internal Q&A systems for employees.

-Domain-specific assistants (medical, financial).

Implementation: It typically involves preparing and indexing data, retrieving relevant information, and integrating it with the LLM query process. It often utilizes vector databases for efficient information retrieval.

Advantages over traditional LLM approach: It combines the strengths of information retrieval systems with LLMs' language understanding; provides more up-to-date and accurate responses; enhances consistency and reduces contradictions in generated text.

RAG is a technique used to improve the accuracy and reliability of large language models (LLMs). RAG has become an industry standard for improving LLM performance and is being adopted by major tech companies and AI researchers to create more reliable and context-aware AI applications

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