What Is a Large Language Model (LLM)?

A large language model (LLM) is a type of artificial intelligence system trained on enormous amounts of data to effectively understand and generate human language text. LLMs are built on a machine learning method called “deep learning” and rely on a set of neural networks called transformers to make semantic connections. 

LLMs have become increasingly popular with the rise of ChatGPT and other generative AI tools, as they can write, summarize, translate, answer questions, and perform dozens of other language-oriented tasks without being explicitly programmed for each task.

The “large” part is not metaphorical. The number of parameters in modern LLMs is measured in the billions or trillions. For example, GPT-4 contains over 1 trillion parameters. These models learn from vast forms of text data, often hundreds of billions of words from websites, articles, books, and other text-based sources. Their advanced language skills come from the fact that both the model size and the training data are extraordinarily vast.

LLMs are “foundation models” in that they’re trained once on general language understanding and used for many different purposes. A chatbot, code generator, sentiment analyzer, or language translator can all use the same base LLM. Because they can do so many things, they’re a crucial part of AI applications across all fields.

How Do Large Language Models Work?

“At a glance, an LLM is built to understand the relationship between words,” says Jeff Curran, data science and analytics expert. LLMs take your words and convert them to predictions and answers through an ordered series of steps.

Tokenization divides your words into small parts called tokens. These can be whole words (e.g., vehicle) or just one part of a word (e.g., “ing” from the word “operating”). These tokens are then converted to numbers so that a computer can perform calculations on each. This technique is how a computer can use a full sentence.

Embeddings change those tokens into math representations that show what they mean. In this mathematical space, words that mean the same thing are close to each other. The model learns that “happy” and “joyful” are connected, but “happy” and “table” are not.

The transformer architecture enables LLMs to derive meaning from embedded data, primarily through a mechanism known as attention. The attention mechanism evaluates and identifies the most critical aspects of the data being analyzed, especially when determining context within a prompt.

For example, when processing “The dog rested on its bed because it was tired,” the model is able to use attention to determine that “it” refers to “dog” rather than “bed.” Additionally, attention allows the model to weigh and prioritize the importance of each word during analysis.

There are two distinct stages for LLMs: training and inference. During the training phase, the model uses vast amounts of text-based data and adjusts its parameters to learn and recognize patterns to predict what follows. During the inference stage, the model has been trained and uses the learned patterns to create new text based on user prompts. Training occurs once and requires massive amounts of computational resources. However, inference occurs every time the user interacts with the model.

Evolution of Language Models: From NLP to LLMs

Statistical models and rule-based systems were the first steps of NLP in the 1950s. These systems used hand-written grammar rules and statistical analysis to figure out what sentences meant and how they were put together. Statistical models were only able to do simple things like check spelling and search for keywords because they couldn’t understand context or the vagueness of human speech.

In the 2010s, neural language models, including LSTMs (Long Short-Term Memory) and Word2Vec, processed information by learning from data rather than relying on specific grammar rules. Neural language models recognized the significance of the relationship between words and of understanding context. However, LSTMs are sequential, which limits speed and their ability to maintain contextualization for long strings of information.

By 2017, transformers had revolutionized the way language was modeled. Unlike previous models, transformers can process an entire string of information simultaneously and use attention mechanisms to model the relationships among all words in the string. 

Due to this ability to process strings of information in parallel, transformer models were trained significantly faster than LSTMs, resulting in major improvements in understanding context and meaning. This technology has enabled the transition from basic autocomplete capabilities to systems that can generate written content en masse, correct coding errors, and engage in conversation.

Common Use Cases of Large Language Models

LLMs are highly functional and offer users a myriad of practical applications. Today, they’re used in a wide range of applications, from chatbots and virtual assistants to data analysis. 

Conversational AI and Customer Support

Support teams, like customer service and tech support, have been using LLMs to assist chatbots and virtual assistants in handling customer questions on a 24/7 basis. The systems created by LLMs can output human-like answers to their questions and also learn from interactions. That means they don’t need to be guided by humans. Additionally, organizations use these LLM models to assist in creating multilingual customer service, which is helping to break down some of the barriers to international customer service.

Content Creation and Writing Assistance

Marketing departments and business professionals use LLMs to create a wide variety of content for advertising, blogs, social media, product descriptions, etc. The speed at which content can be produced is improved while creating a consistent  “voice” across channels. In addition to generating content, sales staff and executives use LLMs to develop their own email correspondence, presentations, and reports by adapting the tone or style appropriate for each target audience and to meet specific requirements.

Text Classification and Extraction

LLMs can be used in many fields and practices to sort text-based data. Customer experience teams can efficiently organize customer feedback and reviews into segments. Attorneys and accountants can distill essential details from contracts, invoices, or compliance documents. These extraction and classification capabilities turn unstructured text into organized data without the need for archaic manual tagging methods.

Summarization

Executives and researchers can use LLMs on long reports or papers (particularly with large amounts of data) to quickly review their findings. LLMs can provide the most relevant key points to quickly assess the main ideas. Researchers also use summaries to rapidly scan large amounts of literature.

Code Generation and Software Development

Developers use LLMs to write code, debug errors, and translate from one language to another. For instance, GitHub Copilot developers can create entire functions based on the comments provided or on partially written code. This allows developers to speed up the time it takes to develop new applications and assists them in learning new programming languages and frameworks.

Knowledge Search and Information Retrieval

LLMs help knowledge workers and support staff search through huge document collections and find exact answers to specific questions. IT teams use them for internal knowledge bases, and legal researchers leverage LLMs to look up case law and regulations. They are also used by technical writers to help them read complicated documents.

Data and Analytics Applications

Decision-makers use LLMs to analyze their data by asking natural language queries rather than writing SQL code. For example, a sales manager could ask, “What was our top product last quarter for the Northeast Region?” and receive an answer right away. Likewise, data teams are using LLMs to help discover trends in contextual data and create insights from customer reviews.

The success or failure of LLMs depends entirely on the quality and consistency of the underlying data. Business analysts and decision-makers can provide trustworthy insights by connecting LLMs to high-quality data through semantic layers. If data quality is lacking, users will receive plausible-sounding but inaccurate answers based on inconsistent metrics.

LLM Analytics: How Large Language Models Are Used in Analytics

The combination of LLMs and analytics is changing the way businesses use their data. Business users can now ask questions and get answers in plain English, even if they don’t know SQL or how to use dashboards.

• Natural language querying of data: Business users can ask, “Which products made the most money in Q4?” instead of writing complicated SQL queries. LLMs turn conversational questions into database queries, democratizing data access across the organization.
• Generating explanations for metrics and trends: When metrics go up or down, LLMs look at the data and explain why in simple terms. They help teams figure out the “why” behind the numbers by pointing out things like changes in customer behavior or seasonal patterns.
• Automating report narratives: LLMs automatically write executive summaries and insights from dashboard data. Instead of analysts spending hours writing monthly reports, the system provides narrative explanations of the most relevant findings.
• Summarizing dashboards and insights: LLMs break down complicated dashboards into easy-to-understand summaries that show decision-makers what they need to know. They can also answer more questions about specific charts or data points.
• Supporting decision-making workflows: LLMs use data patterns to suggest next steps, identify unusual events, and run simulations. They help teams get from insight to action faster by acting as smart assistants.

Where’s the challenge? LLMs can be limited by data quality and consistency. For example, if a user’s LLM input is cluttered with poor-quality datasets and varied metric definitions, the output will be unreliable and inconsistent answers. The solution is a semantic layer. It provides a controlled, single view of the business logic and enables a reliable, strong analytics experience.

LLMs and Structured Data (Where Semantic Context Matters)

LLMs are great at understanding language, but their answers are only as reliable as the data they access. Their insights are accurate when they’re based on clean, well-governed data with clear definitions. They may give you answers that sound “right” when they access messy or unclear data, when, in reality, that answer is completely wrong.

The issue stems from unclear and inconsistent definitions across business metrics and semantics. What does “revenue” really mean in your business? Is it the total revenue, net revenue, or the revenue after returns? Does the word “customer” mean accounts, contacts, or both? An LLM might deliver answers that don’t make sense if it pulls from different sources without clear, consistent definitions.

The answer to correcting these LLM flaws is by way of semantic context. A semantic layer establishes business metrics uniformly across all data sources and tools. It tells the LLM exactly what “revenue” means, how to figure it out, and where the reliable source is. This changes LLMs from advanced language generators into dependable analytical tools that business users can really trust to help them make decisions.

Advantages of Using LLMs

Professionals across many fields, from healthcare to finance, use LLMs to improve their skills and make their work more efficient. LLMs provide many advantages, including:

• Efficiency in handling large-scale data and complex queries: With their broad knowledge base and ability to process and analyze data quickly, LLMs can answer complex queries and summarize information at impressive speeds. Analysts can review thousands of customer reviews in minutes, and executives can digest quarterly reports instantly.
• Scalability across various industries and applications: Because LLMs don’t have to be built specifically for each industry, they offer great flexibility and versatility. The same base technology that’s used to deploy LLMs can be applied across customer service, content generation, code development, and data analysis. 
• Continuous learning and adaptability to new information: LLMs continually improve themselves as they are exposed to new data and interact with users. During this learning process, LLMs continually adapt and improve their ability to meet the organization’s needs without having to completely retrain the model.
• Multilingual capabilities and reduced technical barriers: LLMs can understand and produce text in dozens of languages, which removes a significant barrier to communication for global teams. Additionally, conversational interfaces can access their sophisticated abilities. Marketing teams, sales professionals, and developers can accomplish tasks without specialized technical skills.

LLMs also aid in data democratization by making complex data accessible and understandable. This advantage aligns with the role of semantic layers because both empower non-technical users to interpret data and make informed decisions. They all work together to establish a foundation that anyone can use to work with data confidently.

Limitations, Risks, and Challenges of LLMs

The robust capabilities of LLMs introduce important limitations and risks that organizations must understand. As powerful tools, they bring several critical considerations:

• Hallucinations and inaccurate outputs: LLMs occasionally generate believable but false answers with confidence, referencing non-existent research and inventing fact-based sounding information. There are no inherent fact-checking capabilities in LLMs, so users must manually verify answers, which doesn’t always happen.
• Inherent bias in the training data: Since LLMs are trained using data from the internet, books, and other media, they also inherit the bias present in the data. This can influence the output of the LLM and result in discriminatory recommendations or reinforcement of stereotypes.
• Concerns over data privacy and security: Organizations worry that the training data has adequate protections for user privacy, as it could contain users’ private information, either intentionally or unintentionally. Users who enter their company’s confidential business data into public LLM interfaces risk exposing it publicly.
• Very high cost for computation: To train and use LLMs requires immense amounts of computing power and energy, creating substantial financial and infrastructure barriers. Smaller businesses may not have the resources to support the required computing needs, resulting in inequality between large enterprises and all other businesses.
• Lack of explainability: LLMs operate as “black boxes,” meaning it can be difficult to understand why an LLM produced a specific answer. This lack of transparency presents significant challenges for industries that require audited decision-making processes.

Organizations implementing LLMs must address these limitations through data governance frameworks, human oversight, and careful evaluation of use cases.

The Future of LLMs

LLMs are quickly evolving, adding new features and uses beyond just producing text. There are a number of trends that can affect how businesses use this technology in the next few years.

The next generation of LLMs will be able to handle and create text, images, audio, and video simultaneously. Users will ask questions about charts and get visual answers, or they will explain ideas and get both written summaries and diagrams. This convergence makes AI systems more flexible and easier to use for complex tasks.

General-purpose LLMs are still very powerful, but new models trained on industry-specific data are starting to emerge. Healthcare LLMs are familiar with medical terminology and how things work in clinical settings. Legal models understand both case law and regulatory language. Financial services LLMs look at market data and risk models. These focused models are more accurate for specific use cases, which improves patient care, automates paperwork, and speeds up critical processes in fields like healthcare and education.

LLMs are also becoming agentic systems that act rather than just answer questions. These self-driving workflows can plan tasks that require more than one step, use tools, and carry out processes with minimal human intervention. They can produce reports, set up meetings, update databases, and send out notifications on their own.

At the same time, LLMs are being built into analytics and business intelligence platforms as natural-language interfaces to data warehouses, semantic layers, and data-visualization tools. Users will talk to their data and ask questions, and LLMs will translate their intent into specific actions while adhering to governance rules.

TL;DR: Key Takeaways

• Large language models are AI systems that use transformer architecture to learn and generate human language for a wide range of tasks. They are trained on massive datasets with billions of parameters.
• LLMs break down text into tokens, turn words into math symbols, and use attention mechanisms to figure out the meaning of words and how they relate to each other.
• LLMs automate tasks that used to require an understanding of human language, such as chatbots for customer service, content creation, code generation, and data analysis.
• The information that LLMs use is what makes them reliable. If they don’t have clear definitions and governance through semantic layers, they could give answers that seem right but aren’t.
• Today’s  LLMs enable users to ask questions about their data in a natural way and have them generate answers, reports, and trends based on those questions.
• LLMs can generate false answers with confidence, retain the biases of the training data, and introduce new privacy issues that require human management and oversight.
• Next-generation LLMs will be able to handle text, images, and video. They’ll also be built right into data platforms as smart, conversational interfaces.

Build AI-Ready Analytics on Trusted Data

LLMs represent a powerful shift in how we interact with data, but their effectiveness depends entirely on the quality and consistency of the information they access. Even the most advanced language model can generate inaccuracies, diminishing trust if the data isn’t controlled and the business definitions aren’t clear. A semantic layer solves this problem by providing a single source of truth about metrics and business logic across all of your data platforms.

Solutions like the AtScale semantic layer platform create this foundation, ensuring that when users ask questions in natural language, they get answers grounded in trusted, consistently defined data rather than plausible-sounding hallucinations. Contact us today to schedule a free live demo.