ACCURACY AND RELEVANCE
While an LLM can generate coherent and contextually appropriate responses, it may sometimes provide outdated or incorrect information, especially if the query involves specific, niche, or recent data. RAG solutions like Helm Gen™ retrieve specific, up-to-date information from a database or other knowledge sources before generating a response. This ensures that the output is not just plausible but also accurate and relevant.
SPECIALISATION AND CUSTOMISATION
By integrating retrieval mechanisms, RAG allows businesses to tailor the model to access their own databases, knowledge bases, or industry-specific information. This means that the outputs will be both broad and deeply specialised, rather than generalised.
UP-TO-DATE INFORMATION
Whereas LLMs are only trained on data up until a certain date – say, the launch of the latest model, RAG can dynamically pull in the most current information from a live database, making it ideal for business environments where up-to-date knowledge is crucial, such as fintech, retail and marketing sectors.
COMPLIANCE AND SECURITY
This is a big one. A common risk of an LLM-based solution is that it runs the risk of not complying with certain business regulations, especially within highly regulated industries like banking and the medical field. With RAG, businesses can control their sources, rather than open themselves up to the entire history of the world wide web.
IMPROVED USER EXPERIENCE
By combining retrieval with content generation, businesses can offer more precise and contextually relevant interactions, which improves the overall customer experience.
Don’t get us wrong – LLM-based platforms like ChatGPT and Gemini have changed the world in that they’ve opened our eyes to the sheer power of Generative AI. But in a business context, it’s crucial to consider the benefits of RAG-based solutions like Helm Gen™, which we believe to be the safest form of Gen AI for business.
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