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What is RAG?

Retrieval-Augmented Generation (RAG) is an advanced technique in natural language processing (NLP) that combines retrieval-based and generative approaches to improve the accuracy and relevance of generated content. RAG leverages the strengths of both retrieval and generation to provide more informed and contextually appropriate responses.

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Components of RAG

  1. Retrieval Component: Searches the Knowledge Base for relevant information based on the input query.

  2. Augmentation: Uses retrieved information to enhance the original query with additional context.

  3. Generation Component: Generates responses using a language model, integrating both the query and retrieved information.

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How RAG Powers Our Voice Bot

We leverage RAG to enhance the performance of our voice bot by combining retrieval and generation capabilities. Here’s how it works in our platform:

  1. Query Processing: When a caller provides a query, the RAG framework is initiated.

  2. Retrieval Step: RAG acts as a retriever, searching our structured knowledge base to find matching topics. The knowledge base is organized to optimize RAG’s performance and ensure precise matches.

  3. Generation Step: The language model (LLM) uses the retrieved information to decide on the content to present to the caller, ensuring responses are relevant and context-aware.

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Knowledge Base Structure for RAG

Our knowledge base is designed to maximize the effectiveness of RAG. Each topic in the knowledge base is categorized and includes the following components:

  • Topic: The FAQ name or category of the information.

  • Sample Questions: Example queries that callers might use. These help RAG understand user intent and improve matching accuracy.

  • Content: The information we want the LLM to provide to users.

  • Action: Specific actions triggered by the query, such as calling a function, initiating a workflow, or handing off to a human agent.

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Why we use RAG for GenAI

Using Retrieval-Augmented Generation (RAG) offers several benefits that enhance the performance of our GenAI capabilities:

  • Improved Query Understanding: By including sample questions in the knowledge base, RAG can better interpret user intent and deliver accurate results.

  • Enhanced Accuracy and Relevance: RAG provides informed and contextually appropriate responses by retrieving the most relevant information for each query.

  • Scalability and Efficiency: The framework utilizes existing databases without requiring extensive retraining, making it adaptable to various domains.

  • Customized Domain Responses: RAG can be tailored to specific industries or use cases, ensuring high-quality and domain-specific interactions.

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