How to Cure LLM Weaknesses with Vector Databases

For many years, there has been speculation about the potential impacts of AI on enterprises. Now we’re seeing companies from diverse sectors starting to leverage large language models (LLMs) and generative AI (GenAI). According to McKinsey, the global economy could benefit as much as $4.4 trillion from GenAI adoption, making the use of AI and LLMs more alluring than ever.

Off-the-shelf LLMs are enticing because they are a relatively accessible way to weave general-purpose AI into the fabric of an organization. However, LLMs have one significant deficiency that can offset potential benefits: a lack of domain-specific context. In simple use cases, this may not be an issue. However, in production and other more complex contexts, generic LLMs can create a cascade of challenges.

As businesses increasingly turn to real-time AI applications and tools, they need to transcend these limitations. You might ask how it’s possible to affordably and sustainably augment AI-dominant environments. The answer is vector databases, which I’ll dissect in this post, the first of a two-part series.

Limitations of LLMs for Enterprises

Before delving into the world of vector databases, I’ll look at three significant limitations of off-the-shelf LLMs.

Outdated Training Data

The training data an LLM ingests ultimately defines its capabilities. This is a significant limitation because data is rarely evergreen. Instead, data is often a snapshot of a specific time, meaning there’s a strong possibility it will eventually be irrelevant or incorrect.

Stale and outdated data has significant implications because the accuracy of AI applications wholly depends on the quality and freshness of training data.

Lack of Organization-Specific Context

Training data for off-the-shelf LLMs comes from disparate public and private sources. This data bestows LLMs with all their capabilities. For businesses, the concern is that generic LLMs lack organization-specific context. This is because no off-the-shelf LLM leverages proprietary data specific to a particular enterprise, and this means a variety of unique contexts will go unacknowledged.

AI Hallucinations

Confidence is both a strength and a weakness of LLMs. They have the uncanny ability to answer questions with absolute certainty, even if their answers are completely wrong. This phenomenon, known as AI hallucinations, can result in inaccurate, nonsensical or potentially dangerous outputs.

For businesses whose credibility and operational efficiency hinge on robust and high-quality LLMs, AI hallucinations pose a significant threat. And since off-the-shelf LLMs always run the risk of using outdated or domain-irrelevant data, the threat of AI hallucinations looms large.

Understanding Vector Databases: Vector Embeddings

To understand how vector databases can improve LLMs and other real-time AI applications, I’ll first describe what they comprise.

A vector database is an indexed repository of vector embeddings. Vector embeddings are mathematical or numerical representations of diverse forms of data such as text, videos, photos and audio. By transforming disparate human-readable data into a numerical sequence, vector embeddings provide semantic (rather than superficial) value. Essentially, vector embeddings categorize data based on relationships, context and deep meaning.

Transforming complex semantics within disparate data formats into standardized numerical representations is vital in an LLM context. By using mathematical language and logic, vector embeddings provide a higher degree of search and retrieval accuracy across previously heterogeneous data. This helps optimize searches, clustering, categorizations and anomaly detection. For businesses, this is potentially transformative because any machine learning (ML) algorithm can benefit from vector embeddings.

How Vector Databases Give Off-the-Shelf LLMs a Boost

In off-the-shelf LLMs, vector embeddings used during training typically remain unpublished and unknown, so it’s difficult to assess the limits of their understanding and capabilities. However, most LLMs have embedding features, which means businesses can inject domain-specific data into them to address organization-specific knowledge gaps. By integrating supplemental LLM vector databases comprising vector embeddings of proprietary and other domain-specific information into their LLMs, companies can enhance off-the-shelf AI solutions according to their unique needs.

Enriching and optimizing LLMs with vector databases can also negate the risks of off-the-shelf products that I listed above.

For instance, enterprises don’t have to worry about their LLMs leveraging stale data if there are opportunities to periodically add more updated and relevant data. Furthermore, by adding vector databases with proprietary data, organizations can significantly reduce the possibility of AI hallucinations.

The benefits of AI adoption are not going to be served on a platter. However, by understanding and utilizing LLM vector databases, enterprises can unlock the full potential of robust real-time AI applications.

LLMs and Vector Databases: A Path Forward

There’s been a proliferation of generative AI and LLMs across various sectors. Numerous organizations are leveraging these technologies to strengthen their backend infrastructure, augment services and offerings, and become leaders in their field. While off-the-shelf LLMs are a good starting point for running real-time AI applications, they are rife with challenges and limitations. Key among these are outdated training data, a lack of organization-specific context and AI hallucinations.

Vector databases and embeddings are a powerful antidote to these LLM challenges and can greatly enhance search accuracy.

In part two of this series, I’ll explore how the retrieval-augmented generation (RAG) architectural framework helps companies add proprietary vector databases into their LLMs and AI ecosystems to address the limits of off-the-shelf LLMs.

Learn how Aerospike’s enterprise-grade vector search solution delivers consistent accuracy at scale.