What Is an LLM?

Short Answer

An LLM — Large Language Model — is an AI system trained on vast amounts of text to predict the next word in a sequence. That capability sounds simple, but at sufficient scale it produces models that can answer questions, write essays, draft code, explain concepts, and hold extended conversations. ChatGPT, Claude, and Gemini are all LLM-based. The model is not “thinking” in any human sense — it is making extremely sophisticated statistical predictions about which tokens (roughly, word fragments) should follow which. Understanding this matters because it explains both why LLMs are so useful and why they fail in the specific ways they do.

How LLMs Actually Work

The architecture underneath modern LLMs is the transformer, introduced in 2017 and now the foundation of every major model in use. The simplified version of what happens:

The model is trained on a huge corpus of text — large portions of the internet, books, academic papers, code. During training, the model is repeatedly shown a sequence of text with the last few words hidden, and asked to predict what comes next. Over billions of examples, it learns the patterns of language — grammar, factual associations, reasoning structures, code syntax — encoded into billions of internal parameters.

When you give a trained LLM a prompt, it does the same thing it learned during training: predict the next token, then the token after that, then the token after that, building up a response one piece at a time. There is no database lookup, no execution of logic — just probability calculations over a vast learned space of language.

This statistical-prediction nature explains the behaviours people find both magical and frustrating about LLMs. They are excellent at things that depend on patterns in language: writing in a particular style, summarising, translating, drafting, explaining concepts that appear frequently in their training data. They are weaker at things that require true computation or up-to-date facts: precise arithmetic, knowing the current date, citing real sources accurately. The hallucination problem — generating plausible-sounding wrong answers — is the flip side of the same capability. The model is predicting what should come next, not retrieving what is true.

Modern LLMs add capabilities on top of this core: tool use (the model can decide to call a calculator or search the web), structured output (the model can be constrained to produce JSON or follow a schema), function calling (the model can invoke external APIs), and reasoning modes (the model is encouraged to think step by step before producing an answer). Each of these expands what an LLM can do in practice without changing the underlying prediction architecture.

Why LLMs Matter for Businesses

LLMs have become the single most flexible new capability in software because they handle natural language at a level that was previously impossible. Any business task that involves reading, writing, classifying, or summarising text is now in scope for automation in a way it was not five years ago.

The practical implications are broad. Support tickets can be classified and drafted at scale. Customer questions can be answered from your own knowledge base (combined with RAG). Internal documents can be queried in natural language. Code can be drafted, reviewed, and refactored. Emails, contracts, reports, summaries — all are now in the AI capability set, with varying levels of human review depending on the stakes.

The economic effect is significant. Tasks that used to require a person reading, processing, and responding can now be partly or fully automated, with the person stepping in for the parts that require judgement. This is not the same as “AI replacing humans” — most use cases settle into a pattern where the human’s role shifts from doing the work to overseeing and refining it.

What to Look For

• Model choice for the use case. Different LLMs have different strengths (see Claude vs ChatGPT). The right choice depends on the specific task.
• Cost and latency at production scale. What works for prototyping at low volume may not work at production cost. Model pricing matters.
• Grounding strategy. For business use, the LLM usually needs to be grounded in your data via RAG, function calls, or structured prompts. Out-of-the-box LLM responses are not enough.
• Output validation. Critical actions should not be triggered directly from raw LLM output. Validate, confirm, or constrain.

Common Mistakes

The most common mistake is treating an LLM as a database. It will confidently answer factual questions and many of the answers will be wrong, because it is predicting plausible text rather than looking up facts. The second is failing to constrain the output. An LLM left to generate freely will sometimes produce unhelpful, off-topic, or unsafe content; constraining it with clear instructions and structured output formats is most of the work in building reliable LLM systems. The third is treating “the model is good enough” as the same as “the system is production-ready”. The model is a component; the surrounding engineering is what makes the application reliable.

How We Approach This

We build with LLMs across multiple providers (Claude, OpenAI, others where they fit) and treat them as one component of a larger system rather than the system itself. The engineering work around the model — retrieval, prompting, validation, monitoring — is where most of the quality lives.

Apply LLMs Where They Fit

The services pages below cover AI development work and how we structure LLM-based applications. If you are weighing how LLMs could fit into a specific use case, that is the natural starting point.