How Large Language Models Actually Work: A Plain-English Guide for Business Leaders
How Large Language Models Actually Work: A Plain-English Guide for Business Leaders
If you have used ChatGPT, Claude, or any AI writing tool in the past two years, you have interacted with a large language model. These tools have moved from curiosity to infrastructure for a lot of businesses, and the M&A and capital markets world has been no exception. But most of the explanations of how these systems work are written for engineers or written so simply that they miss what matters most to a business owner or advisor trying to make practical decisions about where to use them and how much to trust them. This piece tries to fill that gap.
You do not need to understand how to build a language model. You do need to understand what they are good at, where they fail, what their outputs actually represent, and why they sometimes produce confident-sounding nonsense. That understanding changes how you use these tools and how you evaluate the work they produce.
What a Language Model Is, at Its Core
A large language model is a system trained to predict the next word in a sequence. That is its fundamental task. Given a string of text, what word is most likely to come next? The model learns this by processing enormous amounts of text, observing the statistical relationships between words and phrases, and encoding those patterns into billions of numerical parameters that get updated during training until the model’s predictions match the training data well.
When you type a question into ChatGPT or Claude and it responds, it is generating that response one token at a time, with each token being selected based on what the model has learned is most likely to follow the tokens that came before it. The output that reads like a thoughtful paragraph is the result of this sequential prediction process, running thousands of times to produce a complete response.
The Core Mechanism
Input text → Tokenization (text split into chunks) → Attention layers (model weighs relationships between all tokens) → Next token prediction → Repeat until response is complete
The “attention” mechanism is what allows the model to understand that “it” in “the document was long but it was worth reading” refers to the document, not the reading. This ability to track relationships across long spans of text is what makes modern language models qualitatively different from earlier approaches to natural language processing.
What Training Actually Does
A language model is trained on text. A lot of text. The GPT-4 series and models of similar scale were trained on hundreds of billions of words drawn from books, web pages, code repositories, academic papers, and other text sources. During training, the model processes this text in batches, makes predictions, compares those predictions to the actual next words, measures the error, and adjusts its internal parameters slightly to reduce that error. This adjustment process, called backpropagation, runs across trillions of examples before the model converges to something useful.
What the model learns during this process is not a database of facts. It learns patterns. It learns that certain words appear together in certain contexts, that certain question formats tend to be followed by certain answer formats, that code in Python tends to have certain structural patterns, and that a sentence beginning with a subject and verb tends to complete itself in predictable ways. The model’s “knowledge” is really a compressed statistical representation of the patterns present in its training data.
This has an important implication: the model does not retrieve information the way a search engine retrieves a web page. It generates text that fits the patterns associated with the input you gave it. When it tells you that a particular law was passed in a particular year, it is not looking that up in a database. It is generating what it has learned tends to follow questions like yours. Most of the time that produces accurate output. Sometimes it produces confident-sounding text that happens to be wrong.
Why Language Models Make Things Up
The technical term for this is hallucination. It happens because the model’s job is to generate text that fits the pattern of its input, not to verify the factual accuracy of what it produces. If you ask a question about a specific case citation, a specific company’s revenue in a specific quarter, or a specific regulatory ruling, the model will often generate a plausible-looking answer even if the underlying fact is wrong, because generating a plausible-looking answer is what it was trained to do.
Hallucination is more common in specific domains. Precise numerical data, specific citations, named individuals and their credentials, recent events that post-date the model’s training cutoff, and highly specialized technical content are all areas where language models hallucinate at higher rates. For business use, this means you should always verify specific factual claims that the model generates rather than treating its output as a primary source. Use the model to draft, structure, and analyze. Verify the facts independently.
Where Language Models Are Reliable vs. Where to Verify
Generally reliable: Drafting, summarizing, restructuring text you provide, explaining general concepts, generating options and frameworks, analyzing documents you share with the model, writing code for common tasks, asking “what questions should I be asking” about a topic.
Always verify independently: Specific statistics and data points, case citations and legal references, named individuals and their roles, recent events and current market data, financial figures for specific companies, and any factual claim the model presents without citing a source you can check.
The Difference Between Models in 2026
Not all language models perform the same way. As of 2026, the frontier models from Anthropic (Claude), OpenAI (GPT-4o and the o-series), and Google (Gemini) represent the state of the art for general-purpose language tasks. These models differ in their training approaches, context window sizes (how much text they can process in a single interaction), and performance across different task types. Some models are better at reasoning through complex multi-step problems. Some are better at following precise instructions. Some have stronger performance on coding versus writing versus analysis.
For most business applications, the differences between frontier models matter less than the differences in how you use them. A mediocre prompt to an excellent model often produces worse output than a well-structured prompt to a good model. Learning to write effective prompts, which means giving the model clear instructions, relevant context, and a specific output format to target, returns more value than switching between tools in search of a marginal quality improvement.
What Context Windows Mean in Practice
A context window is how much text a model can hold in its “working memory” during a conversation. Early models had context windows of a few thousand tokens, which limited what you could feed them. Current frontier models support context windows of 200,000 tokens or more, which is roughly the length of a full novel. This has practical implications for M&A and advisory work. You can now feed a model an entire purchase agreement, a full data room of financial documents, or a lengthy CIM and ask it to analyze, summarize, or answer specific questions about the content.
The important nuance is that performance is not uniform across the context window. Models tend to perform better on content at the beginning and end of their context and sometimes miss or underweight content in the middle of a very long input. For high-stakes document analysis, the best practice is to break long documents into sections, process each section, and synthesize the results rather than dumping everything into a single interaction and assuming the model captured it all equally.
How Language Models Are Changing M&A Work
In M&A advisory specifically, language models are having their most immediate impact on the work that was previously most time-intensive for junior analysts: document reading and summarization, initial draft preparation, research synthesis, and first-pass analysis. A model can read a 200-page purchase agreement and identify the non-standard provisions in minutes. It can review a data room of contracts and flag customer concentration risk. It can draft the first version of a CIM section from a set of financial data and management interview notes. It can produce a competitive landscape overview from a set of web research inputs faster than any analyst working manually.
What it cannot do is replace the judgment that determines whether a deal is worth pursuing, how to structure an offer, how to read a room when negotiations turn difficult, or whether the management team presenting in a management meeting is telling you the whole story. The leverage that language models create in deal work is concentrated in information processing and document production. The irreplaceable human value remains in strategy, relationships, and complex judgment calls. Understanding that distinction is what separates advisors who use these tools well from those who either ignore them or over-rely on them.
For a practical look at how AI tools are being integrated into the M&A advisory workflow right now, see The AI Tools Changing How M&A Deals Get Done in 2026. And for how business owners specifically can use AI to prepare for a transaction, see How to Use AI to Prepare Your Business for Sale.
