Introduction
Retrieval-Augmented Generation, or RAG, is a powerful tool that many businesses—from startups to established companies—are not leveraging, to their detriment. Valere took a special interest in this innovative tool back in the final months of 2023 thanks to one of our close technology partners. Since then, it’s been a transformative journey creating nine platforms for startups at various funding stages, from Series Seed to Series D, to legacy businesses, giving them all a unique leg up on their competition. These RAG applications go beyond simple chat functions, it’s much more powerful than that. They act as a business’s compass for strategic planning, streamlining process, and automation. Prepared to provide responses to queries that range from generating profit and loss statements, to crafting job postings, and analyzing cost drivers like Customer Acquisition Costs (CAC).
What exactly is RAG?
In layman’s terms, it’s an AI framework where the system first hunts down the relevant information from vast data reserves and then employs this data to formulate responses with precision and insight. Think of it as a super-librarian that not only knows where every piece of information is stored but has the wisdom to understand it deeply enough to provide a summary, analysis, or even a forecast.
But how does RAG work?
It’s a symphony in five steps
1. Prompting: A user makes a request or prompt. 2. Retrieval: A specialized model searches for information relevant to the user’s request.3. Augmentation: The retrieved data is crafted into a coherent prompt.4. Generation: A general Large Language Model (LLM) uses the crafted prompt to generate a response.5. Output: The system delivers a refined answer, integrating the information collated in the preceding steps. Initial implementations can answer straightforward questions, but it’s the potential for intricate, context-aware responses that should get you excited about its arrival. By refining retrieval processes, engineering more effective prompts, and enhancing the architecture, we aim for RAG to deliver deep, actionable insights. Analogous to a super intelligent, wise consultant who has seen it all, who understands every last facet of the business and can magically make connections across vast amounts of data. In refining steps two (2) through the four (4) shown above, the system will be able to give accurate answers to more advantageous questions such as “how can we reduce our CAC?,””Is there any audience that we are not selling to that we should,” or “what is the most efficient size for our engineering team.” In other words, the AI will be able to increase the value of its outputs, in terms of accuracy, breadth of knowledge, and connections within data – as the RAG framework is continually optimized.Harness the Power of RAG with Valere
RAG isn’t just a fleeting trend. It’s the next chapter in AI’s evolving narrative for businesses to gain an advantage. For Valere, it’s a journey filled with boundless potential and great anticipation for what’s to come. RAG is here to redefine business intelligence, and we are at the forefront of this revolution.
