How to use vector databases to make up for the weaknesses of LLM

[Live broadcast preview] Will large models replace programmers? "

Vector databases enable enterprises to cost-effectively and sustainably adapt general-purpose large language models for organization-specific use.

Translated from How to Cure LLM Weaknesses with Vector Databases , author Naren Narendran.

For years, people have speculated about the potential impact of artificial intelligence on businesses. Now, we are seeing companies from different industries starting to leverage large language models (LLM) and generative artificial intelligence (GenAI). McKinsey believes that the global economy could benefit as much as $4.4 trillion from the adoption of GenAI , making the use of AI and LLM more attractive than ever.

Off-the-shelf LLMs are attractive because they are a relatively easy way to incorporate general artificial intelligence into organizational structures. However, LLM has a significant drawback that may offset the potential benefits: a lack of domain-specific background. In simple use cases this may not be a problem. However, in production and other more complex contexts, general-purpose LLM can create its own set of challenges.

As businesses increasingly turn to real-time AI applications and tools, they need to move beyond these limitations. You may ask how an AI-led environment can be augmented in an affordable and sustainable way. The answer is vector databases , which I'll dissect in this article, the first of a two-part series.

Limitations of LLM for businesses

Before diving 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Outdated training data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 determines its capabilities. This is a significant limitation because data is rarely evergreen. Instead, data is often a snapshot of a specific point in time, which means it may eventually become irrelevant or incorrect.

Old and outdated data has a significant impact because the accuracy of AI applications depends entirely on the quality and freshness of the training data.

Lack of organization-specific context

Training data for off-the-shelf LLMs come from different public and private sources. These data give LLM all its functions. Worryingly for businesses, generic LLMs lack organization-specific context. This is because no existing LLM leverages proprietary data specific to a particular enterprise, meaning that various unique contexts will not be recognized.

Artificial Intelligence Illusion

Confidence is both a strength and a weakness of LLM. They have the uncanny ability to answer questions with absolute certainty, even if their answers are completely wrong. This phenomenon, known as AI hallucination , can lead to inaccurate, ridiculous, or potentially dangerous output.

For businesses whose credibility and operational efficiency depend on strong and high-quality LLM, the AI illusion poses a significant threat. And since off-the-shelf LLMs always run the risk of using outdated or domain-irrelevant data, the threat of AI illusion looms.

Understanding vector databases: vector embeddings

To understand how vector databases can improve LLM and other real-time AI applications, I will first describe what they contain.

A vector database is an indexed repository of vector embeddings. Vector embeddings are mathematical or numerical representations of data in various forms such as text, video, photos, and audio. Vector embeddings provide semantic (rather than superficial) value by converting disparate readable data into sequences of numbers. Essentially, vector embedding classifies data based on relationships, context and deep meaning .

In the context of LLM, it is crucial to convert complex semantics in different data formats into standardized numerical representations. By using mathematical language and logic, vector embedding provides a higher degree of search and retrieval accuracy across previously heterogeneous data. This helps optimize search, clustering, classification and anomaly detection. This is potentially transformative for enterprises, as any machine learning (ML) algorithm can benefit from vector embeddings.

How vector databases improve off-the-shelf LLM

In off-the-shelf LLMs, the vector embeddings used during training often remain unpublished and unknown, making it difficult to assess the limitations of their understanding and capabilities. However, most LLMs have embedded capabilities, meaning that businesses can inject domain-specific data into them to address organization-specific knowledge gaps. By integrating a complementary LLM vector database containing vector embeddings of proprietary and other domain-specific information into their LLM, companies can enhance off-the-shelf AI solutions based on their unique needs.

Enriching and optimizing LLM with vector databases also eliminates the risks of off-the-shelf products listed above.

例如，如果可以定期添加更多更新且相关的数据，那么企业不必担心其 LLM 利用陈旧的数据。此外，通过添加包含专有数据的向量数据库，组织可以显著降低 AI 幻觉的可能性。

The benefits of AI adoption will not come easily. However, by understanding and leveraging the LLM vector database, enterprises can unlock the full potential of powerful real-time AI applications.

LLM and vector databases: the way forward

Generative AI and LLM are proliferating in various fields. Many organizations are leveraging these technologies to strengthen their backend infrastructure, enhance services and products, and become leaders in their fields. While off-the-shelf LLMs are a good starting point for running real-time AI applications, they are fraught with challenges and limitations. Key among these are outdated training data, lack of organization-specific context, and AI illusions.

Vector databases and embeddings are powerful antidotes to these LLM challenges and can greatly improve search accuracy.

In Part 2 of this series, I'll explore how the Retrieval Augmented Generation (RAG) architectural framework can help companies add proprietary vector databases to their LLM and AI ecosystems to address the limitations of off-the-shelf LLM. *Learn* how Aerospike's enterprise-grade vector search solution__ delivers consistent accuracy at scale.

This article was first published on Yunyunzhongsheng ( https://yylives.cc/ ), everyone is welcome to visit.