Enterprise AI With Retrieval Augmented Generation: AI Technology Beyond LLMs

Discover how retrieval augmented generation (RAG) can revolutionize generative AI in your enterprise. We will cover what is RAG, how RAG is transforming workplace technology and productivity, and gain a deeper understanding of how it overcomes the limitations of using only large language models (LLMs), making enterprise AI more dependable, accurate, and efficient.

LLMs lack awareness of your enterprise

LLMs offer strong reasoning and language-generation capabilities. They can easily write imaginary stories, generate term papers about Dickens, and so much more. However, they lack an understanding of business-specific terminologies, workflows, and strategies that are unique to your enterprise. Despite LLMs' competence, this is creating barriers for widespread, successful enterprise AI implementations.

Imagine having an AI model with no understanding of your company's unique context in charge of writing your emails, presentations and press releases. Would you trust it?

RAG: a breakthrough in AI contextually-aware reasoning

Enter RAG—this technique combines the storage, retrieval and understanding of enterprise knowledge with LLMs, improving quality and specificity of generated output. Picture an LLM without RAG as a writer without specific knowledge, and an LLM with RAG as that writer guided by a researcher providing vital references and data sources. The better the RAG algorithm, the better the output. Rather than expensive and time-consuming training or finetuning of LLMs, RAG permits instant incorporation of enterprise knowledge, ensuring factual correctness while controlling for hallucinations. RAG blends cost efficiency with excellent results, significantly reducing capital costs compared to retraining and tuning.

RAG and LLMs: A promising partnership in AI technology

RAG with LLMs is yielding promising outcomes in enterprise AI. It supports the idea that enterprise LLMs can incorporate company knowledge, continually updated for its latestness and relevancy. Using LLMs without RAG, means you will struggle with:

• Speed: Initiating the tuning of an LLM can take months of data preparation, plus substantial capital costs. Plus they are instantly outdated the next time new data arrives or is generated by your enterprise.
• Inflated costs: LLM-only systems depend on larger models for superior outputs, but these larger LLMs also incur higher operational costs. 
• Nuances in sophistication: The accuracy of AI-driven responses may be compromised due to insufficient enterprise data awareness – causing hallucinations.

The merging of RAG and LLMs provides:

• Superior LLM responses: LLMs that have data incorporated with RAG generate more accurate and meaningful AI responses.
• Improved cost-effectiveness and efficiency: RAG allows enterprises to shift their focus on retrieval and lower the burden of compute on the LLMs. This enables enterprises to run smaller, cheaper LLMs.

Investing in enterprise AI platforms that leverage both RAG and LLMs is a smart move. As a result, the efficiency and quality of these platforms surpass singular stand-alone models, dramatically improving costs while concurrently improving generative results—making it the move for any forward-thinking enterprise.

Stay competitive with Yurts

RAG is revolutionizing the world of AI and paving the way for more advanced enterprise deployments of generative AI. RAG, working in tandem with LLMs, is paving the way for higher-quality and cost-efficient enterprise AI technologies.

Understand how best-in-class RAG systems can empower your business. Request a free demo of Yurts Enterprise AI today.

‍