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Retrieval Augmented Generation (RAG)

June 14, 2023
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1. The Everyday Struggle of Traditional Search

Picture this: Jo works in the insurance department of a sizable company, navigating the vast sea of internal documents. They're on a quest for specific information: the standard deductible amount for their Comprehensive Car Insurance policy for vehicles valued under $30,000. Sounds straightforward, right? Not quite. Jo types "Insurance Policy" into the search bar, only to be greeted by a maze of links that lead nowhere near the desired information. Eventually, a colleague comes to the rescue with the right link. It's a scenario many of us in large organizations know all too well - a frustrating and time-consuming drain on our productivity.

2. Enter RAG: The Smart Search Solution

Now, reimagine that scenario with Jo using RAG, a cutting-edge search tool. Jo simply types in their question:

RAG Demo

And voila! The AI, equipped with advanced language models and vector databases, promptly displays the answer with references. It’s not just cool; it’s a game-changer for efficiency and ease of access to information in the workplace. The buzz around this technology is massive, and it's easy to see why.

3. Behind the Scenes of RAG

RAG operates through three core steps: Indexing, Retrieval, and Generation.

  • Indexing: Here, company documents are cataloged into a Vector Database. This process either happens offline or continuously in the background, ensuring data is always up-to-date.

  • Retrieval: When Jo asks a question, the system converts it into a vector using an embedding model, then fetches the most relevant documents from the indexed database.

  • Generation: Finally, with the right documents in hand, the system crafts a prompt for a Large Language Model (LLM), which then generates a comprehensive response, often including handy hyperlinks or document snippets.

4. Gold Rush
The expansive application scope of RAG technology, which is relevant to virtually every enterprise globally, has led to a surge in startups developing RAG solutions and infrastructures. Additionally, every tech giant like Google, OpenAI, AWS, Microsoft, and others are also creating RAG systems and offering them through their platforms. It is anticipated that RAG will become the most widely available and standardized/commoditized software in the Generative AI market, making the deployment of RAG solutions for any enterprise remarkably straightforward. This area is expected to become one of the most densely populated in technology.

5. The Future
Building effective RAG solutions requires navigating a complex landscape, filled with various approaches and implementation methods. Critical factors such as managing hallucinations, controlling costs, structuring prompts effectively, and setting up robust infrastructure are just the beginning. Equally important are the challenges of debugging, monitoring, and ensuring safety, security, and privacy. Additionally, supporting rich text formats like tables and charts, as well as handling multimedia content like images, audio, video, and multi-modal elements, add layers of complexity. Without the guidance of experts and the support of a solid infrastructure, RAG solutions can struggle to progress from development to successful production. This underscores the necessity of working with experts and reliable infrastructure in the development of RAG solutions to ensure they are effective and production-ready.

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