What are large language models (LLMs)?
Large language models (LLMs) are a type of artificial intelligence (AI) that interprets human language and generates text-based content. If you’ve ever had a conversation with an AI-powered model like ChatGPT or Google’s Gemini, you’ve interacted with an LLM.
Beyond casual conversations with AI, businesses implement LLMs to automate workflows, speed up decision-making, and innovate processes across many industries. Let’s expand on the definition of LLMs to show how this technology streamlines your business operations.
LLM definition
A large language model employs deep learning techniques to understand and generate text that mimics human language. LLMs rely on transformer models — a type of neural network that’s trained on large datasets to learn language patterns, context, syntax, and meaning. For example, if you ask AI to summarize a pitch, the LLM will recognize that “pitch,” in this case, means a sales presentation, not a baseball move.
In the definition of LLM, the term “large” refers to the massive number of parameters (often in the billions or even trillions) the model uses to provide coherent text across various contexts. You can implement LLM-powered AI in content discovery, text generation, and more.
Types of LLMs
Large language models vary according to their purpose, design, and training methods. While there isn’t a strict classification, common types of LLMs include:
- General-purpose models are broad LLMs that don’t specialize in any specific task but handle a wide range of text-based functions, such as language translation, AI summarization, and document retrieval
- Fine-tuned models start as general-purpose models and are refined by developers to perform specific functions, such as assisting in customer service
- Zero-shot models perform tasks without needing specific examples of those tasks in their training data
- Multimodal models incorporate not only text but also images, audio, and video, making it possible to describe a picture or analyze a video clip
- Language representation models are typically used to understand context and meaning in data analysis by identifying relationships between words
LLM vs. generative AI vs. RAG: What’s the difference?
Simply put, there’s no competition between these terms, as they all fall under the umbrella of AI technology. Generative AI (GenAI) is a broad category that includes any AI technology that creates new content, whether text, images, or music — while LLMs are a specific type of GenAI focused on generating text.
Retrieval-augmented generation (RAG) combines the power of LLMs with content retrieval, enhancing the accuracy and relevance of your queries. For example, when you use an Intelligent Content Management solution to create, organize, and store your data in the cloud, each technology plays a different role.
- GenAI allows you to create content, such as emails, meeting agendas, and marketing copy
- RAG enables you to ask questions about documents within your cloud-based data storage and retrieve specific files, delivering more contextual, up-to-date information based on your content — rather than relying only on LLM training data
- LLMs make these actions possible by processing, interpreting, and generating the content
Top benefits of LLMs for businesses
From scaling content creation to expanding enterprise AI strategies, businesses use large language models to optimize their operations and gain a competitive edge. According to Grand View Research, the global LLM market size was estimated at $4.35B in 2023 and is expected to grow at a CAGR of 35.9% from 2024 to 2030.
Explore the main benefits of LLMs for your operations.
- Improved content discovery and search: LLM and RAG allow you to quickly find information by asking questions in natural language. This capability is valuable when you work with a large amount of data and extensive contracts or reports, preventing your teams from spending significant time searching through documents to locate snippets of information.
- Better use of unstructured data: Per PwC research, 70% of CEOs globally say GenAI will significantly change how their companies create, deliver, and capture value in the next three years. LLM value comes from the ability to understand and deliver insights from content. By tapping into unstructured data, you enhance your company’s strategies and deliver more impactful customer experiences.
- Enhanced task automation: A Predibase survey reveals that 58% of organizations adopted LLMs, while 35% said their primary use case involves GenAI capabilities, such as content generation and summarization. Automation reduces the need for manual effort when writing a message for a customer or reviewing content, for example.
Check out how to get started with enterprise workflow automation.
The best LLM use cases for enterprises
Aside from content creation, what are LLMs used for? From advanced search to question and answer within documents, businesses are expanding the ways they employ this technology to get quick results. According to a Box-sponsored ESG white paper, 72% of AI adopters saw value from their AI initiatives within three months, with 11% experiencing immediate benefits.
Let’s review LLM examples you could implement in your organization.
LLM use cases | LLM examples |
Document search and retrieval |
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Information summarization |
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Content production |
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Q&A |
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Information retrieval |
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Language translation |
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How do large language models work in content management?
Managing your data lifecycle involves creating, organizing, and storing content to make the most of its value. According to a global study by Deloitte, 75% of organizations increased their technology investments around data lifecycle management due to GenAI, employing LLM capabilities across many uses.
To understand how this process works in content management, note how an advanced cloud solution integrates LLM in AI.
1. Training on large datasets
One critical aspect of large language modeling involves training systems on massive amounts of text data sourced from websites, articles, and other forms of written content. This data enables models to capture nuances like tone and sentiment. The training process exposes the LLM to sequences of words and phrases, allowing it to learn how words are likely to follow one another.
2. User query
You input a query or request when asking an LLM to answer a question or retrieve a document. The clarity and context of the query determine how the model will process the input.
3. Data transmission
Once you submit the query, the system transmits the input data to the LLM for processing. In advanced cloud content management solutions, this step involves sending the original question and its most relevant information to an LLM from an external provider — encrypting the information to ensure secure transmission.
4. Data processing
The LLM then processes the query by analyzing its language structure. Based on its understanding of the context, the LLM identifies relevant information to provide an answer. In this step, the AI provider relies only on its own memory, without using external data sources.
5. Response delivery
Next, the AI system develops the answer to your query, often including citations to identify the sources of the information, which helps verify the accuracy of the response. You can also retrieve a document summary or a list of relevant files.
6. Continuous learning and improvement
As more users interact with the system and feed the model with increasing volumes of information, the LLM continues improving over time. This learning process allows the model to refine its responses and adapt to new types of queries, content, and user behaviors. For example, it might refine its tagging based on your industry-specific jargon or adjust content suggestions based on your preferences.
Follow these best practices to achieve more precise LLM outcomes over time.
Use metadata to enhance data context and relevance
A key strategy for creating intelligent content powered by AI is incorporating strong metadata to enhance its searchability. Metadata management makes it easier for AI to provide more precise answers by defining the attributes of your content, including keywords, categories, and descriptions.
Explore metadata management best practices to optimize your content lifecycle.
Integrate RAG to prevent LLM hallucination
LLM hallucination happens when a model generates information that isn’t true or makes up answers that don’t exist. Remember that the LLM doesn’t “know” the information; it creates responses based on patterns learned during training. Even though the output sounds convincing, the model might give an answer that isn’t backed by real data or facts.
To avoid this problem, integrate techniques like RAG, which helps the model double-check facts by retrieving data from your reports, policies, manuals, and project documentation. This way, LLM gives more precise answers, minimizing the chances of errors.
Integrate LLM and artificial intelligence into your content workflows with Box AI
Box has built sophisticated large language models into our intelligent and secure content platform. With the Intelligent Content Cloud, you can leverage document creation, storage, collaboration, workflow automation, and zero-trust security capabilities to keep your information protected and compliant.
We created Box AI to help you make the best use of your unstructured content. Box AI enables you to draft documents, summarize information, and get answers to your questions in seconds. Our integrations enable you to connect content across 1,500+ applications, giving you one secure place to edit and store your most important information.
Reach out to our team and let’s discuss the power of the Intelligent Content Cloud.
Note: The information provided in this article is for general informational purposes only and should not be considered legal advice or relied upon to make any legal or compliance decisions. The content of this article is not intended to create an attorney-client relationship, and readers should consult with a qualified attorney or compliance professional for specific legal or compliance advice tailored to their individual circumstances.