Artificial Intelligence 101. What is a Large Language Model?
Article 1
The Definition of AI Generative Text Systems
This article series aims to take technical jargon and break it down into understandable language.
Artificial Intelligence is the acceptable marketing term for the underlying tool called a large language model or LLM. The term large language model is exactly what its title implies, a computer program built to handle human communication on a truly massive scale. Stripping away the technical confusion, this tool functions at its most basic level by accepting a phrase or a question and responding to it. Once it receives this input, the system responds by assembling text based on patterns found in its training data which consists of massive amounts of human writing, audio, concepts, images and videos.
To see how this works, it helps to compare these large language models with traditional software applications. Standard computer programs, such as calculator tools or basic spreadsheets, operate under rigid rules where pressing a specific button will always deliver the exact same predefined outcome. A large language model deviates from this path because it reacts dynamically to instructions, granting it the ability to write out an answer in thousands of different ways. Because of this flexibility, the final output depends entirely on how you frame your request albeit those responses may not always be accurate.
Navigating this subject becomes much easier once you grasp a few foundational terms. Here are some common references and their definitions to help translate common industry technical jargon into plain language:
- Algorithm: A set of rules or instructions a computer follows to solve a problem.
- Context Window: The strict limit on how much text the system can remember in a single conversation before it starts forgetting older instructions.
- Data Set: A massive collection of text documents used to teach the system language patterns.
- Fine-Tuning: A secondary training step where the system is given specific examples to help it specialize in a particular topic or tone.
- Generative AI: A category of technology focused on creating brand new content, such as text or images, rather than just analyzing existing files.
- Hallucination: When the system confidently generates false or completely invented information.
- Prompt: The specific text or question a user types in to get a response.
- Token: A small chunk or fragment of a word. The system breaks sentences down into these smaller pieces to process language efficiently.
Here are a couple of websites that lead to more articles that heavily explain AI in detail. Be full warned, they are filled with technical jargon.
- https://www.databricks.com/blog/what-are-large-language-models
- https://www.ibm.com/think/topics/large-language-models
How These Systems Predict Words
To understand how a large language model works, think about the text messages you send every day. When you type "I am on my", your smartphone keyboard likely suggests the word "way" as the next logical choice. This is the core concept of text prediction in action. These advanced artificial intelligence systems operate on a very similar, albeit massively scaled-up, version of that everyday autocomplete feature.
When you type a prompt into the interface, a large language model relies entirely on the context of your specific instructions to figure out what should come next. The program breaks your sentence down and uses complex mathematical equations to guess the most appropriate word to follow. It strings these calculated guesses together, one word at a time, to build a complete response tailored to your request.
Because every single word is chosen based on statistical probability, a large language model is essentially determining the path of highest likelihood to form a coherent sentence. It does not pull from a hidden database of pre-written answers or follow a static script. Instead, the software fluidly builds its reply from scratch during every single interaction by predicting the most natural-sounding text sequence.
This purely predictive approach explains why a large language models struggle with basic math, occasionally outputting that one plus one equals fifteen instead of actually calculating the sum. In the past, a very popular way to test this limitation was to ask the AI how many times the letter R appears in the word strawberry. Prior to this test becoming popular, the AI would fail almost every time to answer 3. In today's world, if the program outputs the correct answer, it is not because it possesses an internal engine computational engine capable of counting letters. It provides the correct answer of 3 r's solely because that specific response is all over the Internet and is now heavily baked into its training data as the most likely response. An AI does not understand you, it is merely predicting what you want to hear with varying degrees of success.
The Meaning Behind "Large" in Language Models
When people talk about a large language model, the word "large" does not refer to the physical size of the computers running the software. Instead, it points directly to the staggering volume of reading material the system consumed during its development phase. Programmers feed these systems billions of pages of text, including books, articles, and websites, so the software can analyze human vocabulary at a massive scale. By reviewing such an immense amount of written information, the application learns exactly how words fit together in almost any context imaginable.
Because a large language model is exposed to such an immense scope of human writing, it develops an impressive sensitivity to different styles, tones, and forms of communication. The software can effortlessly shift from generating a formal corporate press release to drafting a playful birthday card, simply because it encountered those distinct patterns repeatedly during its initial reading phase. This massive breadth of training data means a single program can recognize regional idioms, professional jargon, and subtle cultural nuances without requiring developers to build separate, specialized software applications for each unique writing task.
Common Misconceptions About Artificial Intelligence
It is easy to assume that interacting with a sophisticated computer program feels like talking to a person who remembers your past conversations. However, a large language model does not have any internal concept of time. It exists purely in the exact moment a prompt is processed. Once you close a chat window or start a new session, the system has no ongoing memory of what happened in the past, nor does it possess any awareness of the future. There is even a context window in the same chat where the AI does not recall what is in its own chat. Every single request is treated as a completely isolated event. Some AI interfaces have built memory storage for it, but these are random at best and tend to interfere with your future chats. For example, if the AI chose to store information about rats from a conversation in the past, it will reference that memory and spit out information about rats in a completely different conversation about apple pie.
Because the text generated can sound incredibly natural, another frequent myth is that the machine actually feels, thinks, or understands the words it is writing in the way a human being does. In reality, a large language model is completely void of emotion or intellect. When it writes a sympathetic apology or an enthusiastic product description, it is not experiencing those feelings. It is simply replicating the patterns of human emotion it analyzed during its initial development, without truly grasping the actual meaning behind the vocabulary.
Ultimately, the application lacks consciousness, personal intent, and actual comprehension of the subjects it discusses. You might ask a large language model to draft a complex strategy, but it does not actually know what the plan is designed to achieve. Instead of forming its own independent thoughts, the system operates entirely as a mirror of mathematical data, reflecting back the most probable combinations of words based on your instructions.
Setting Realistic Expectations for Outputs
Because a large language model is built entirely around guessing the next logical word, it can easily struggle with factual accuracy. The application prioritizes how natural a sentence sounds over whether the information is actually true. If inaccurate information appeared frequently in the training data it originally studied, the system will confidently present that inaccurate information as an absolute fact simply because the mathematical probability points in that direction.
This major limitation makes an internal human review process critical before finalizing, sharing, or publishing any generated documents. You should never assume that an output produced by a large language model is ready to be sent to a client or posted on your company's website directly from the prompt window. Having an honest and knowledgeable person verify the details ensures that the material remains trustworthy.
Think of the AI as an untrained intern with access to a near infinite amount of information that they don't understand and will provide you with inaccurate information consistently you will not be disappointed. The large language model will never understand your point of view, regardless of the chat window response. It will never truly understand your business or your goals despite outputting something very convincing. The reason it output that very convincing information is because it accurately guessed you want the same thing as tens of thousands of others that are contained within its training data. This means you need to check over every single thing that the AI or large language model outputs because it will get it wrong.
How Humans Guide the System
The way you interact with a large language model dictates the results you receive. Think of your initial user input as a map that guides the prediction engine toward a specific writing style. Without clear directions, the software will simply guess at the destination, which often leads to generic or confusing text. By providing detailed instructions, you effectively steer the application to produce content that perfectly matches your intended tone.
Ultimately, the quality of the final output relies heavily on the clarity, context, and constraints provided by the person typing the request. A large language model cannot read your mind to figure out what your company's audience needs to hear. If you want a concise, upbeat email, you must explicitly state those parameters. The more background details and strict boundaries you offer, the more accurately the program can align its mathematical guesses with your actual goals. Being too wordy can cause token exhaustion and also result in inaccurate or an undesired response. We will break down tokens in another article.
Many beginners struggle because they are simply too brief, leading to frequent errors when crafting these instructions. To help you avoid early pitfalls, it is useful to analyze the differences between weak commands and effective guidance. The table below highlights common mistakes people make when prompting a large language model and provides concrete examples of how to improve those requests for much better results.
Mistake: Being too vague.
- Poor Example: Write an article about computers.
- Effective Example: Write a 500-word introduction defining computer hardware for middle school students.
Mistake: Lacking specific context.
- Poor Example: Fix this email.
- Effective Example: Rewrite this email to a client so it sounds more professional and apologizes for the delay.
Mistake: Ignoring formatting constraints.
- Poor Example: Give me ideas for dinner.
- Effective Example: Provide a numbered list of five vegetarian dinner ideas that take less than 30 minutes to prepare.
Professor Casey Fiesler has videos on YouTube that talk about Large Language Models. You can view them here: Large Language Model Videos.
