Organizations integrating LLMs (Large Language Models) into their workflows are surpassing the competition with powerful AI tools for knowledge-base management, text and image generation, and data analysis. But pre-trained LLMs like ChatGPT have significant limitations, putting businesses at risk with inaccurate responses, hallucinations, and out-of-date data. That’s where RAG, or Retrieval-Augmented Generation, comes in.
A RAG system is an AI framework designed to enhance the capabilities of LLMs. RAG-based LLM architecture — which leverages a vector database and a trusted, continually updated data source — ensures that your LLM is content aware and delivers accurate responses customized to your domain. But RAGs can be costly, difficult to set up, and time consuming to maintain.
That’s why we’re building the Shakudo RAG Stack, a turnkey RAG-based LLM framework that you can easily set up and start running in minutes. Shakudo integrates with the top LLMs and vector databases, giving you immediate access to best-in-class tools that make the most sense for your business. In four simple steps, you will be up and running with a RAG framework that’s securely connected to your trusted data source.
Shakudo offers the fastest, most cost-effective way to self-serve a best-in-class RAG system. Click here to learn more and to sign up for early access.
[.div-block-152][.text-block-45]Learn About Shakudo's Production-Ready RAG Stack[.text-block-45][.cta-button-blog]LEARN MORE[.cta-button-blog][.div-block-152]