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Boosting AI with Retrieval Augmented Generation: Accuracy and Reliability Unleashed

July 9, 2024

Understanding  artificial intelligence can help people better appreciate the capabilities and limitations of modern AI systems, as well as the potential future developments in AI technology.

One such revolutionary AI method is Retrieval Augmented Generation (RAG). This advanced natural language processing technique offers valuable insights.

RAG is a method for enhancing the accuracy and reliability of generative AI models by incorporating facts from external sources. It's particularly useful for improving the performance of language models like ChatGPT.

How RAG Works

1. Traditional language models predict the next word based on previous input.

2. RAG first retrieves relevant information from provided documents.

3. It then generates responses based on both the query and the retrieved information.

This approach allows AI to answer questions about specific documents or data that weren't part of its original training set.

Common RAG Applications

1. Company-specific information: RAG can help AI systems answer questions about internal documents, policies, or data not available publicly.

2. Web search integration: Some AI assistants use RAG to provide up-to-date information from the internet.

3. Text summarization and completion: RAG can identify important sections of documents or relevant chat history.

Cognistx's Use of RAG

Our products, SQUARE and SQUARY, uses RAG for its question-answering system. This is crucial for risk-sensitive domains where answer validation is essential. Square not only provides answers but also cites specific sources within documents, enhancing reliability and transparency.

The Future of RAG

1. Reducing AI hallucinations: RAG is one of the most effective techniques for ensuring AI responses are grounded in factual information.

2. Personal AI systems: Future applications could include AI assistants that can access and understand personal documents on your computer, providing a more intuitive way to interact with your data.

3. Enhanced privacy: By processing data locally, RAG-based personal AI could offer powerful functionality without compromising user privacy.

The evolution of RAG and similar technologies represents a shift towards more intuitive human-computer interaction. Instead of navigating complex file systems or remembering specific commands, users can simply ask questions in natural language and receive accurate, contextual responses.

To learn more about how Cognistx's can revolutionize your business using AI, schedule a meeting with Cognistx CEO Sanjay Chopra or email

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