Business Development & Marketing SpecialistOctober 19, 2026
An LLM, short for large language model, is an AI model trained on very large amounts of text so it can read a piece of text and produce new text in response. It answers questions, summarizes long documents, translates, writes code and continues a conversation. When you use a chat assistant such as ChatGPT, Claude or Gemini, an LLM is running behind it.
A quick note on the term: LLM also stands for Master of Laws (LL.M.), a postgraduate law degree. This guide is about the AI meaning.
Below we cover what an LLM is, how it works, the key terms you will meet, common model families, what LLMs are used for, where they fall short, and how teams integrate them into products. Because Neon Apps builds products with AI features, the last section looks at the topic from a product team’s point of view.
What Is an LLM and What Does LLM Stand For?
LLM stands for large language model. The word “large” points to two things: the amount of text the model learned from and the number of parameters inside it. Parameters are numerical values the model adjusts during training, and together they shape every answer it gives. Modern LLMs have billions of parameters or more.
To place the LLM definition in context, it helps to look at the layers of AI. Artificial intelligence is the broadest field. Machine learning is the part where computers learn from data. Deep learning is machine learning done with many-layered neural networks. An LLM is a very large neural network trained on language.
LLM and generative AI are also not the same thing. Generative AI covers every model that can produce new content, including text, images, audio and video. An LLM is the part of that field that works with language. If you are comparing the two for a project, our guide to choosing generative AI development services covers the wider picture.
How Do LLMs Work?
At its core, an LLM does one simple job: given some text, it predicts what is most likely to come next. It repeats this prediction token by token until the answer is complete.
For example, if the text begins with “The capital of France is”, the model assigns a high probability to “Paris” as the next piece and picks it. It then repeats the same step to continue the sentence. Long, coherent answers are built from that single step repeated thousands of times.
The architecture behind this is called the transformer, introduced in 2017. A transformer weighs how every part of a text relates to every other part at the same time. That is how a model can connect a word at the start of a paragraph to a word at the end.
Training usually happens in two stages:
Pre-training: The model learns to predict the next piece of text across a very large collection of text. It picks up grammar, facts and writing patterns here.
Alignment and fine-tuning: The model gets extra training, often including human feedback, so it follows instructions and gives safe, useful answers. This stage turns a raw model into a usable assistant.
When an LLM answers, it does not look the answer up in a database. It generates new text based on patterns learned in training. This difference matters when we get to its limits.
Key LLM Terms to Know
Six terms come up constantly when you work with LLMs.
Tokens. The unit the model processes text in. A token can be a word, part of a word or a punctuation mark. Pricing and limits are calculated in tokens, and the same text can use more or fewer tokens depending on the language.
Parameters. The numerical values the model learned during training. The count is often linked to a model’s capacity, but it is not a quality score on its own.
Context window. The amount of text the model can consider at once. Your input, the conversation history and the answer all have to fit inside it. A larger window lets you give the model longer documents.
Prompt. The instruction and input you give the model. The same model can produce very different quality depending on how the prompt is written.
Temperature. A setting that controls how much variety the model shows. Low values make answers more consistent and predictable. Higher values make them more creative and varied. Tasks like classification usually call for a low value, and brainstorming for a higher one.
Multimodal models. Models that accept more than text, such as images and audio. Features that describe a photo or answer questions about a screenshot rely on them.
LLM Examples and Model Families
LLMs fall into two broad groups. Closed models are used only through the provider’s API or interface. Open-weight models have downloadable weights, so you can run them on your own servers or computer, which is often called a local LLM.
The best-known model families are:
GPT, developed by OpenAI and the model behind ChatGPT.
Claude, developed by Anthropic.
Gemini, developed by Google.
Llama, released by Meta as open-weight models.
DeepSeek and Mistral, among other providers whose models are widely used.
ChatGPT is not an LLM itself. It is a product built on top of one. The same model family can power many different products and apps.
A local LLM makes sense when data must not leave your organization. The trade-off is that hardware, maintenance and updates become your responsibility. For the first version of most products, starting with an API is faster and carries less risk.
Which model to choose depends on the workload more than on brand preference. We cover this in detail in which LLM you should build your app on and in our guide to choosing between Claude and OpenAI by task.
How to Compare LLMs
LLM benchmark results and leaderboards measure a model’s general ability. They do not measure the workload inside your product. A model can lead a general test and still do worse on your documents, your rules and your users’ language.
So compare models on your own data, using a few criteria:
Accuracy: Test with real examples from your product.
Language quality: Measure answer quality in the languages your users actually write in.
Cost: Calculate the token price against the usage volume you expect.
Latency: Consider how long your users will wait for an answer.
Privacy and license: Check where data is processed and what the usage terms allow.
LLM Use Cases
LLMs are used across most work that involves language:
Summarization: Turning long documents, meeting notes and reports into short summaries.
Classification: Sorting support requests, emails and reviews by topic.
Question answering and search: Letting people search company documents or product information in natural language.
Writing and translation: Drafting text, changing tone and translating between languages.
Code: Helping write, explain and debug code.
Chat and assistants: Powering conversational AI products that talk with users.
Agents: Deciding the next step in systems that call tools to reach a goal. We explain this in what is an AI agent.
LLM Limitations
LLMs are powerful, but they have limits worth knowing.
Hallucination. A model can state something false with confidence. Its goal is to produce likely-looking text, not to look up the truth.
Outdated knowledge. A model knows what it learned up to its training date. It needs an extra source to know anything newer.
Context limits. Information outside the context window is invisible to the model, and quality can drop with very long inputs.
Privacy. Sending personal data to an external API calls for a review under the data protection rules that apply to you, such as GDPR.
Inconsistency. The same question can get slightly different answers at different times. Tasks that need consistency need a layer that checks the output.
Bias. A model can carry biases from its training data. Features shown directly to users need regular review of outputs.
Cost and latency. Larger models cost more and respond more slowly, and the difference shows up in your budget as usage grows.
How to Integrate an LLM Into Your App
Adding an LLM to a product often starts with a single API call. The real work lies in the decisions around that call. Four approaches are used most often:
Prompting: You describe the model’s role, rules and output format clearly. It is the fastest and cheapest way to start.
RAG (retrieval-augmented generation): Before the model answers, you retrieve relevant passages from your own documents and hand them to it. It is a common way to get current, sourced answers and reduce hallucination.
Fine-tuning: You train the model further on your own data. It is considered when a specific style or format is needed, and it is not the first need of most products.
Tools and agents: You let the model connect to your systems and take actions. The approach that standardizes these connections is explained in Model Context Protocol for product teams.
At Neon Apps, we start from the workload when we add AI features to a product. We first define what the feature will do, how accurate it must be and at what volume it will run, and only then pick the model. The business side of that approach is covered in AI transformation: what it means and how to get started and measuring AI ROI.
If you are planning LLM app development or want to add an LLM to an existing product, take a look at our AI transformation services and our AI-based product solutions, or get in touch to talk through your idea.
