Understanding RAG (Retrieval Augmented Generation)

One of the key techniques behind the recent evolution of chatbots and intelligent conversational agents is a technique known as Retrieval Augmented Generation (RAG), which combines the power of LLMs and NLP with the ability to augment them with data from your organisation for better context and response accuracy, specific to your business.

RAG has proven to be a significant step forward in enhancing the conversational capabilities of AI agents. It combines two techniques: (i) Retrieval, which locates information that is relevant to a given context from a vector database of your company’s private information, and (ii) Generation, which generates meaningful responses from the available information using the power of the underlying LLM technology. In doing so, RAG can generate responses that are not only contextually appropriate but also linguistically fluent. This is a significant improvement over simpler models that use templated responses or rules-based approaches. The technology can be integrated into chatbots, personal assistants, and virtual agents that can interact with customers seamlessly and provide appropriate responses in a quick and timely manner.

Retrieval-augmented generation can address many of the current limitations of generative AI by increasing accuracy and transparency. It has the potential to revolutionize the way we communicate with AI. Typically ,organisations are building RAG capabilities on top of public APIs such as OpenAI, however this can often lead to organisations not wanting to upload sensitive documentation for fear of data leaks. Using open-source LLM (Falcon/LLaMA) models allows organisations to build up secure and private AI capabilities which is then fine-tuned on the company’s internal documentation.

JedAI provides all the necessary functionality to enable organisations to create a custom and secure RAG + Open Source LLM AI platform fine-tuned on secure company data.

Talk to us today to learn more about real-world applications of RAG and the technology solutions we can provide to aid your AI journey.