In the first part of this series, I highlighted the ever-increasing adoption of generative AI and large language models (LLMs) by organizations in all sectors and regions. Companies have a strong conviction that real-time AI applications are powerful engines that can help them boost digital performance, edge past competitors in saturated markets, build stronger customer relationships and improve profit margins.
According to Gartner, multimodal AI models, featuring diverse data and media formats, will dominate six out of 10 AI solutions by 2026. The limitations of generic LLMs, such as outdated training data, lack of organization-specific context and AI hallucinations, are roadblocks to high search accuracy and performance in these AI models. However, as I discussed in first part of this series, by using vector databases, businesses can mitigate these challenges and boost their AI applications.
Retrieval augmented generation (RAG) is an architectural framework that leverages vector databases to overcome the limitations of off-the-shelf LLMs. In this article, I will walk you through the functions and benefits of RAG and how it can facilitate a radical makeover of LLMs and real-time AI environments. However, before I discuss the benefits of RAG, I’ll touch on another common solution for addressing LLM limitations: fine-tuning.
Two Ways To Address LLM Limitations
Although RAG is one of the most effective ways to overcome the limitations of LLMs, it isn’t the only solution. I discuss both below.
Fine-Tuning
Fine-tuning involves taking a preexisting and pretrained LLM, such as an off-the-shelf solution, and putting it through more rounds of training. Enterprises can fine-tune LLMs ad hoc or periodically, depending on their needs.
Fine-tuning typically involves smaller or ultra-specific data sets. For example, businesses in health care or education might want to fine-tune a generic LLM to suit their environment’s specific needs.
While fine-tuning is a powerful option, it’s time-consuming and resource-intensive, making it an unaffordable option for many.
Retrieval Augmented Generation (RAG)
RAG is an architectural framework that helps businesses use proprietary vector databases as a precursor step in their LLMs and AI ecosystems and processes. RAG uses these search results as additional input to the LLM that can be leveraged to shape its answers. RAG sharpens the accuracy of LLM results by providing highly contextualized, real-time, enterprise-specific data through these external vector databases.
Crucially, RAG allows companies to do this without retraining their LLMs. A RAG architecture enables LLMs to tap into external databases before creating a response to a prompt or query.
By sidestepping retraining processes, RAG offers businesses an affordable and convenient way to enhance their AI applications without compromising search accuracy and performance.
The Functions and Benefits of RAG
Now that you have a basic understanding of what RAG is, I’d like to shift the focus to its key functions and primary benefits.
Better Search Quality
Enhanced search quality is one of the first benefits that businesses unlock by using RAG. Generic pretrained LLMs have limited search accuracy and quality. Why? Because they can only do what their initial training data sets enable. Over time, this can result in inefficiencies and either wrong or inadequate responses to queries.
With RAG, businesses can expect more layered, holistic and contextualized searches.
Inclusion of Proprietary Data
Another benefit of using RAG comes via enriching LLMs with additional data sets, particularly proprietary data. A RAG model ensures that this proprietary data, standardized as numeric vectors in an external vector database, is accessible and retrievable. This provides LLMs the ability to handle complex and nuanced organization-specific queries. For example, if an employee poses a question that’s specific to a particular project, professional record or personnel file, a RAG-enhanced LLM can retrieve this information without much hassle. The inclusion of proprietary data sets also reduces the risk of LLMs eliciting hallucinated responses. Businesses, however, must establish robust guardrails to maintain security and confidentiality for themselves and their users.
There are also other less obvious, albeit just as powerful, benefits of using RAG. By enhancing search quality and including proprietary data, RAG allows businesses to diversify the ways they leverage their LLMs and apply them to pretty much any use case. It also helps businesses make the most of their in-house data assets, which is an incentive to actively optimize data management ecosystems.
Looking Beyond RAG
RAG can help generate better, more contextualized and hallucination-free responses to humans’ questions. With RAG, a chatbot’s response to a user is faster and more accurate. Of course, this is just one simple use case. There’s an immense proliferation of generative AI and LLMs across disparate industries and geographical swaths. Therefore, there’s also endless potential to optimize AI applications using vector databases.
Many future scenarios and use cases require subsecond decision making, unparalleled search accuracy and holistic business context. Vectors, particularly through the power of similarity search, are the key to success in those scenarios. Consider use cases like fraud assessments and product recommendations. These harness the same principles of rapid vector processing for enhanced similarity and context. This validates that LLM vector databases can enable swift and relevant outcomes in diverse settings.
There’s no limit to what enterprises can achieve with vector databases. Most importantly, vector databases ensure that no organization feels like it can’t be a part of the AI revolution.
Preventing LLM Roadblocks
AI adoption is becoming widespread, and multimodal LLM models are becoming the norm. Against this backdrop, companies must ensure that the traditional limitations of LLMs don’t cause major roadblocks. Search accuracy and performance are a must, and businesses need to continuously look for ways to boost off-the-shelf LLMs and negate their challenges.
While fine-tuning is a potential solution, it’s often expensive and time-consuming. Not all companies possess the kind of resources required to regularly fine-tune generic LLMs. Retrieval-augmented generation is a more affordable, accessible and efficient way to transcend LLM limitations and help businesses augment their AI ecosystem with external data sets.
The key benefits of RAG include better search quality, the ability to include proprietary data sets and more diverse use cases for LLMs.
While RAG is a powerful model to strengthen AI environments, constant advancements in the LLMs and vector database sphere suggest that real-time AI environments are in their infancy: The future is overflowing with possibilities.
Learn how Aerospike’s enterprise-grade vector search solution delivers consistent accuracy at scale.
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Retrieval-augmented generation facilitates a radical makeover of LLMs and real-time AI environments to produce better, more accurate search results.