What is an LLM (Large Language Model)?

The tech behind tools like ChatGPT, Claude, and Gemini? LLMs, of course.

No matter your profession, understanding LLMs is becoming less of an option and more of a necessity.

So, let’s catch you up:

Large Language Models Explained 

A Large Language Model, or LLM, is a type of artificial intelligence model specifically designed and trained to understand and generate human-like language. You could think of them like a very articulate autocomplete system.

Why? LLMs predict the next most probable word in a sequence, creating coherent and contextually relevant text. The “Large” in LLM refers to two things: the immense amount of text data it’s trained on, often spanning billions of words from books, articles, and websites, AND the vast number of parameters (internal variables the model adjusts during training) involved.

An LLM’s predictive power is a staggering leap from traditional Natural Language Processing (NLP) models. You see, older NLP methods relied on more rigid, rule-based systems or simpler statistical models. While they could perform tasks like sentiment analysis or keyword extraction, they struggled with understanding nuance, context, and certainly couldn’t generate fluid, human-like text from scratch like modern LLMs can.

Modern LLMs, with their unprecedented scale and advanced architectures, excel at understanding the subtleties of language, handling complex contexts, and possess a generative capability that allows them to produce “original” content, hence the name “generative AI.” For instance, an older NLP model might identify keywords in a lengthy article, but an LLM can summarize that entire article into a concise, readable paragraph, preserving its core meaning and flow.

How Do Large Language Models Work?

An LLM’s capabilities stem from three areas: the training process, transformer architecture, and, collectively, tokens, parameters, and its model size.

The Training Process

Imagine a diligent student who has read, memorized, and analyzed billions of books, articles, and websites, far more than any human ever could. Simplistically, that’s what happens during the training process of a Large Language Model. LLMs learn by analyzing vast amounts of text data to identify intricate patterns in grammar, syntax, facts, and the complex relationships between words and concepts.

Their learning often happens through something called “self-supervised learning.” Meaning, the model learns from the data itself, without needing explicit human labeling for every single piece of information. It might do this by trying to predict a missing word in a sentence or the next word in a sequence.

Repeatedly doing this across an unfathomable scale of text, the model develops its vast linguistic knowledge and predictive power. The sheer scale of this data is hard to overstate. Think petabytes of information, allowing the model to grasp nuances that escape smaller systems.

Transformer Architecture Simplified

Underneath all that training data lies the transformer architecture, the foundational neural network design that powers most modern LLMs. Before transformers, older models struggled to understand long-range dependencies in sentences, for instance, how a word at the beginning of a paragraph might relate to one at the end.

The workaround? The “attention mechanism.” When the AI processes a sentence, its attention mechanism allows it to look beyond words sequentially. Instead, it can weigh the importance of every other word in the sentence (or even a whole paragraph) when processing a single word.

Okay, and? Well, this allows the AI to understand deep contextual relationships and nuances that were previously impossible, leading to much more coherent and contextually relevant outputs. It enables an LLM to complete complex sentences or summarize entire articles accurately. Pretty cool!

Tokens, Parameters, and Model Size

Tokens are the building blocks that an LLM uses to process and generate. These are typically parts of words, whole words, or punctuation marks.

For example, the two sentences you just read contain 155 characters and consist of 31 tokens. As a general rule of thumb, one token is typically the equivalent of 4-5 words, 155 characters divided by 31 is within that ballpark: 5.

Tokens directly relate to something called the context window, the maximum amount of text an LLM can consider or “remember” at any one time. You could think of the context window as the AI’s short-term working memory. When you input a prompt or as a conversation with an AI continues, all the text falls within this window, measured in tokens.

If a conversation or document exceeds this limit, the AI might “forget” earlier parts, leading to less coherent or accurate responses. A larger context window allows the LLM to process more information simultaneously, often leading to a better understanding of context and more coherent, detailed outputs.

The “brain” of the LLM itself is defined by its parameters. These are the billions or even trillions of internal variables that the model adjusts and refines during its training. They represent the model’s learned knowledge and its capacity to recognize patterns and generate text.

Lastly, we have model size, which largely refers to the number of these parameters. Generally speaking, the more parameters an LLM has, the “larger” and often more complex and capable it is. It’s for this reason that you hear about models with “billions” or even “trillions” of parameters. It speaks to their vast learning capacity and ability to handle more nuanced and intricate tasks.

5 Most Important LLMs You Should Know About

There are literally thousands of AI models, but there are a handful that you should be aware of:

• GPT-4 (OpenAI): GPT-4 and its newer iterations like GPT-4o are renowned for their broad capabilities, versatility, and strong performance across a wide array of tasks. They excel in complex reasoning, coding, and creative content generation, often serving as a powerful general-purpose foundation for many applications.
• Claude (Anthropic): Developed with a strong focus on AI safety and ethical alignment, Claude models (like Claude 3 and its variants) are known for their strong conversational abilities, longer context windows (meaning they can process and remember more text at once), and robust performance in complex reasoning tasks. They’re often favored for enterprise-level applications where reliability and controlled outputs are important.
• Gemini (Google): Gemini stands out for its multimodal capabilities, meaning it can natively understand and process various types of information beyond just text, including images, audio, and video. Integrated into Google’s ecosystem, it’s designed for versatile applications that combine different data formats.
• Llama (Meta): Unlike the proprietary models above, Meta’s Llama series (e.g., Llama 3) is significant because it’s open-source. Meaning, developers and researchers can freely use, modify, and distribute it. Llama models are highly popular for fine-tuning and running locally, offering flexibility and control.
• Mistral (Mistral AI): An emerging European player, Mistral AI has gained traction in the market for its powerful and remarkably efficient models (like Mistral Large and Mixtral). They are known for striking an excellent balance between performance and computational efficiency, often outperforming larger models in specific benchmarks, which makes them popular for both open-source and commercial applications.

Here’s a simplified comparison:

What Can LLMs Actually Do? (Real-World Applications)

Here are some key areas where LLMs are making a significant impact:

Content Creation and Optimization

For anyone involved in content, LLMs are proving to be invaluable assistants. They can dramatically speed up the initial phases of content production and help refine existing material.

• Generating various content formats: From drafting blog posts and articles to crafting catchy social media captions or persuasive email drafts, LLMs can provide a strong starting point.
• Rewriting, summarizing, and expanding text: Need to condense a lengthy report into a digestible summary? Or perhaps expand a few bullet points into a detailed paragraph? LLMs excel at transforming existing text.
• Brainstorming and outlining: Overcome writer’s block by using an LLM to generate fresh content ideas or structure detailed outlines for any topic.

SEO Applications

While LLMs don’t directly “do SEO” by themselves, they are powerful tools that can assist SEO professionals with different tasks:

• Assisting with keyword research: Although they don’t have access to real-world data like a keyword research tool like Ahrefs, LLMs can help identify related terms, understand the nuanced intent behind keywords, and even suggest long-tail variations you might miss.
• Drafting title tags and meta descriptions: Quickly generate optimized titles and meta descriptions that align with search intent and character limits.
• Creating FAQs based on content: Feed an LLM your article, and it can generate a list of relevant frequently asked questions that users might have, improving content comprehensiveness.
• Analyzing SERP intent: LLMs can help infer the primary intent behind a given query by understanding the context of search results.

Business Operations

LLMs are streamlining countless day-to-day business processes, improving efficiency and customer interactions:

• Customer support chatbots: Powering intelligent chatbots that can handle FAQs, provide basic troubleshooting, and guide customers through common queries, freeing up human agents for more complex tasks.
• Automating internal communications: Assisting with drafting internal memos, summarizing long email threads, or preparing meeting recaps.
• Summarizing long documents or meeting transcripts: Quickly extracting information and actionable points from lengthy text, which can save employees hours of reading time.

Research and Data Analysis

LLMs are also proving invaluable in extracting insights from unstructured text data, making research and analysis more efficient:

• Extracting key information from unstructured text: Sifting through thousands of customer reviews, survey responses, or legal documents to pull out specific data points or common themes.
• Summarizing research papers: Quickly generating concise summaries of complex academic papers, allowing researchers to quickly grasp the findings.
• Identifying trends in large text datasets: Analyzing vast quantities of text, such as social media conversations or news articles, to identify emerging topics, sentiment shifts, or market trends.

5 Limitations of LLMs You Need to Be Aware Of

LLMs aren’t the be-all and end-all. They’re tools, not infallible oracles. Here are five drawbacks to keep in mind:

1. Hallucinations: Perhaps the most talked-about limitation, LLMs can sometimes confidently generate information that is entirely false, nonsensical, or made-up, known as “hallucination.” Because they predict the most probable next token based on patterns, they can sometimes string together plausible-sounding sentences that aren’t rooted in fact.
   • Example: Asking an LLM for specific citations for a fictional study, and it confidently provides legitimate-looking but fake author names, journal titles, and publication dates. Always fact-check!
2. Training Data Cutoffs: LLMs only “know” what they were trained on, and this training data has a specific cutoff date. Meaning, they inherently lack access to current, real-time information or events that occurred after their last training update.
   • Example: An LLM might be unable to provide details about a recent major sporting event that happened last month or discuss the latest product launch from a prominent tech company if its training data predates those events.
3. Bias: LLMs learn from the vast datasets they’re trained on, and if those datasets contain biases (which most human-generated data does), the LLM can inadvertently reflect and even perpetuate those biases in its outputs, which can manifest in stereotypes, unfair representations, or skewed perspectives.
   • Example: An LLM, when asked to summarize a historical event, consistently emphasizes certain perspectives while downplaying or omitting others, revealing a bias in the narratives it learned from its training data.
4. Lack of True Understanding/Reasoning: Despite their impressive conversational abilities, LLMs don’t possess true understanding, consciousness, or reasoning in the human sense. They excel at pattern matching and prediction, not genuine comprehension or critical thought. They don’t “know” facts; they statistically model relationships between words.
   • Example: While an LLM can generate a coherent essay on a complex philosophical topic, it doesn’t understand the philosophy or truly believe in the arguments it constructs. It’s extrapolating from its training data.
5. Consistency and Reliability Issues: For the same prompt, an LLM can sometimes produce inconsistent outputs. While techniques like temperature settings can influence this, achieving absolute reliability and consistency, especially for nuanced or creative tasks, can be a challenge. There’s an inherent probabilistic element to their generation.
   • Example: Asking an LLM to write a 100-word product description twice might yield two slightly different versions, or one might be excellent while the other requires significant editing, even with identical prompts.

Conclusion and Next Steps

Remember, LLMs are powerful tools, not magic.

Grasping that moves you from a casual user into a savvy operator, ready to integrate AI effectively and responsibly across your content, SEO, business, and research.

Now, go forth and build more intelligent, impactful workflows, you savvy AI operator, you.