An “AI” RAG is a smart helper that finds and brings together the best pieces of information to answer your questions quickly and accurately. In other words, Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources (pdf, doc, txt, ppt, html…) that are relevant to your industry.
ChatGPT, Gemini and other generic LLMs look smart to understand context but are not experts in your sector. For that, they would have to know everything about your business.
They are able to recognize patterns and answer questions. But GPTs lack domain-specific knowledge. LLMs “hallucinate” answers 20% of the time. These generic LLMs have inconsistencies and superficiality that are not appropriate for an industrial application.
For commercial applications, you need a RAG. A RAG is able to search for specific information in an existing knowledge base, by combining an information retrieval algorithm with an LLM which will be able to understand the query and provide a detailed response to the user.
A RAG gives you domain-specific knowledge, low hallucination rate and consistency.
A RAG is an expert model that can understand and improve the accuracy of answers in your specific domain. Comparing a RAG to a generic LLM is like asking a question to a 20-year-old expert versus a freshly graduated PhD.
For industrial applications, you need an expert AI-agent. An expert agent is a knowledge assistant that has a specific purpose.
Domain expertise is the key to AI success in the physical world. The expert agent is able to understand, plan tasks. The expert agent is able to divide tasks into subtasks. Each task has 4 steps: Observe, Orient, Decide, Act, Assess. The Expert Agent prioritizes and plans tasks to solve complex problems.
Your RAG combines GPT content generation with information retrieval from your qualified data.
Your RAG enables your organization to enhance the accuracy, efficiency, and relevance of responses while reducing deployment costs and complexity. Your RAG is customizable, flexible, and cost-effective, designed to ensure the security and confidentiality of your data.
This innovation positions your RAG as a strategic asset to leverage advancements in generative AI, save time, and boost overall efficiency within your organization.
Updating your RAG is almost free. RAG requires fewer training resources compared to traditional models.
Your RAG can integrate new information seamlessly without requiring full retraining of the model. Simply update the source files serving as the knowledge base.
This ensures your organization stays aligned with the latest data, essential in a constantly evolving environment.
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