Custom Gen AI models for every need.
We build Industry-Specific LLMs using Retrieval Augmented Generation (RAG) With RAG, businesses can enhance search and information retrieval systems, ensuring more relevant and accurate results. These techniques empower organizations to adapt AI models to specific tasks, making them versatile tools across different industries and sectors.
What is Retrieval Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is an approach that enhances the output of a large language model by using external knowledge sources to generate responses. Large Language Models (LLMs) are powerful tools for answering questions, translating languages, and completing sentences. RAG takes this a step further by using an organization's internal knowledge base or specific domains to improve the accuracy and usefulness of LLM output without having to retrain the model.
Imagine tinkering with a chatbot like it's a modular gadget. RAG lets you swap out its information sources on the fly, like changing batteries. Need it to answer medical questions? Plug in a medical database. Handling customer service? Connect it to your knowledge base. This flexibility makes chatbots more reliable and adaptable, opening doors for organizations to embrace generative AI in all sorts of exciting ways.
Cost-effective implementation
Chatbots usually start with a ready-made language model (like a pre-built brain). Training these models with new data for each company takes forever and costs a lot. RAG is a shortcut that lets us use these models without all the extra training, making them easier and cheaper for everyone.
Benefits of RAG
Current information
Even if a chatbot's initial training covers what you need, keeping it up-to-date is difficult. With RAG, you can easily feed it fresh info like research, stats, or news. Think of it as plugging the chatbot into live news feeds or social media, so it always has the latest info for you.
Enhanced user trust
RAG lets chatbots show their work. It can include citations and references in its answers, so you know where the info comes from. If you need more details, you can even look up the source documents yourself. This builds trust and makes you feel more confident in the chatbot's answers.
More developer control
Developers can easily tweak their information sources, security settings, and troubleshooting features. This level of control makes chatbots more adaptable, reliable, and trustworthy, giving companies the confidence to use them for a broader range of tasks.
How Guardian Insight AI helps implement enterprise-ready RAG.
Guardian Insight AI creates industry-specific RAG-based AI models fed with data from relevant databases, files, and websites to do the heavy lifting of data analysis so you can strategize for your goals and get clear answers quickly without the headache.
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