TL;DR
- An LLM generates text by predicting the next token repeatedly; it learns in 2 stages, pre-training on large text sets, then fine-tuning on instruction examples plus human feedback on answers (RLHF).
- GPT (OpenAI), Gemini (Google), Claude (Anthropic) and Llama (Meta, open-weight) are well-known LLMs; model names change with each new release.
- Answers come from training data with a fixed cutoff, or from web search and RAG; RAG reduces hallucinations but does not remove them.
- For GEO, pages that answer in the first sentence of each section, carry tables and verifiable facts, and are crawlable are easier for AI systems to cite.
- Before staff use LLMs, check each plan's data-retention and training terms and Thailand's PDPA obligations for any customer data entered.
An LLM is a Large Language Model, an AI program trained on a vast amount of text until it can read, write, summarise, translate and answer questions in human language. It works by predicting the next likely piece of text, one step at a time, until it has built a full sentence and a full answer. This article explains, without assuming you are a programmer, how an LLM works, how it differs from generative AI and chatbots, which examples exist, where its answers come from, why it sometimes gets things confidently wrong, how businesses should adjust SEO and GEO as people ask AI instead of searching, and what data risks to watch when using it inside an organisation.
What is an LLM (large language model) and how does it work, without the programming
The easiest way to understand an LLM is to think of the word suggestions on a phone keyboard. Type "good" and the phone may offer "morning" or "night". An LLM does something similar on a far larger scale. It looks at all the text in front of it, calculates what is likely to come next, picks that piece, and repeats the process until it has written several paragraphs.
Text is split into small units called tokens, which can be words, parts of words or punctuation. The model does not read letters the way a person does; it works with these tokens as numbers. Thai, which has no spaces between words, tends to be split into tokens differently from English, which is one reason a Thai passage of the same length can use a different number of tokens than an English one.
An LLM's abilities come from two main stages.
Stage 1: Pre-training
The model is fed an enormous amount of text from many sources, such as public web pages, books and other documents, depending on what each developer chooses, and is trained to guess the next part of the text billions of times. Every wrong guess nudges the model's internal values, called parameters. Repeated enough, the model picks up the patterns of language, commonly stated facts and the styles of reasoning that appear in the text. The "large" in Large Language Model refers to the very high number of parameters and the volume of training data.
Stage 2: Tuning it to follow instructions
A model that has only been through the first stage can continue text, but it does not yet answer like an assistant. Developers train it further on examples of good questions and answers, and use feedback from real people on which answers are better (a technique known as RLHF), so the model answers the question asked, stays polite and declines harmful requests. The result is the chat assistant most people use.
Keep in mind that an LLM does not "look up" answers in a database the way a search engine does. Fundamentally, it generates new text from the patterns it learned. That explains both its strength (fluent writing in any format) and its weakness (it can be wrong while sounding sure), covered below.
How an LLM differs from generative AI and a chatbot
These three terms are often used interchangeably, but they sit at different levels. Generative AI is the broadest term and covers any AI that creates new content, whether text, images, audio or video. An LLM is a subset of generative AI that specialises in language. A chatbot is the interface of a program that talks with users. Some chatbots run on an LLM behind the scenes, but many older chatbots run on rules written in advance, such as "if the customer types 'price', send this message", and involve no LLM at all.
The table below compares the three ideas.
| Term | Meaning | Example use |
|---|---|---|
| Generative AI | AI that creates new content: text, images, audio, video | Creating an illustration from a description, generating a voice-over |
| LLM | Generative AI that works with language, trained on large amounts of text | Drafting an email, summarising a report, translating a document |
| Rule-based chatbot | A conversational program that replies according to pre-written conditions | An auto-reply menu where users tap to pick a topic |
| LLM-powered chatbot | A chat interface that sends messages to an LLM to generate replies | An AI assistant you can question in natural language |
Many models now accept more than text, such as images or document files. These are called multimodal models, but underneath they are still language models.
Examples of widely used LLMs
The best-known examples are listed below. Each developer releases new versions regularly, so model names change quickly.
- GPT, developed by OpenAI, is the model family behind ChatGPT.
- Gemini, developed by Google, is used in the Gemini app and across Google services.
- Claude, developed by Anthropic, is available through the Claude app and through an API.
- Llama, developed by Meta, is an open-weight model that organisations can run on their own systems under its licence terms.
Models differ in many ways: ability on particular tasks, how much text they can take in at once (the context window), API pricing, data policies and Thai-language ability. No model is best at everything. The practical approach is to test candidates on your organisation's real work before choosing.
Where an LLM's answers come from, and why it hallucinates
Source 1: Knowledge from training data
What a model knows without searching comes from its training data, which has a training cutoff. Anything that happened after that date is unknown to the model. Ask about the latest product price or yesterday's news, and a model without web search can only answer from older data or say it does not know.
Source 2: Web search and RAG
Many AI assistants can search the web. The system finds relevant pages, inserts their content into the text sent to the model, has the model write an answer from that content, and often shows source links. This general technique is called RAG (Retrieval-Augmented Generation). Organisations use the same RAG approach with internal documents, such as product manuals or company policies, so an AI assistant answers from the organisation's own information instead of guessing from general knowledge.
Why LLMs hallucinate
A hallucination is when a model produces information that sounds right but is not true, such as citing a book that does not exist or giving a number with no source. The cause lies in how the model works. It is trained to produce text that is "likely to come next", not text that has been checked as true. When it meets a question its training data covers thinly or not at all, it can still produce an answer shaped like a real one. A confident tone therefore says nothing about accuracy.
RAG and web search reduce the problem because the model has real documents to draw on, but they do not eliminate it. The model can still misread a document, pick an unreliable source, or conclude more than the source says. The safe practice is to have a person check every number, name, date and legal point before it is used.
How businesses should adjust SEO and GEO as people ask LLMs instead of Google
When a user asks an AI assistant and gets an instant summary, the path to information changes. Some people read the answer and stop without clicking any website. Some click through to the sources the AI cites. The approach called GEO (Generative Engine Optimization) is about getting your brand and content picked up and cited in those answers. Here is what a business can do:
- Get basic SEO right first: most AI systems that search the web have to find content before they can cite it. Pages blocked from crawling, slow to load or poorly structured lose out in both traditional search and AI answers.
- Answer the question directly in the first paragraph: RAG systems pull content in short passages. A page that answers clearly in the first sentence of each section is easier to use than one where the reader has to get through the whole page to find the answer.
- Include verifiable facts: tables, steps, specifications and clear definitions are content that systems can cite easily.
- Keep brand information consistent everywhere: business name, services, address and key details should match across the website, social profiles and directories so the information AI sees does not conflict.
- Use structured data: schema markup helps search engines understand which pages are articles, FAQs, products or business information.
- Track how AI talks about your brand: ask several AI assistants the questions customers are likely to ask, and record whether your brand is mentioned and whether the information is correct. Answers change over time and between users, so check again periodically.
More detail on this approach is on the Relevant Audience service pages for GEO (Generative Engine Optimization) and AI SEO. Businesses interested specifically in appearing in ChatGPT answers can see ChatGPT SEO.
Data risks when using LLMs in an organisation
Using an LLM in daily work can save a lot of time, but there are risks to manage before opening it up to all staff.
Leaking confidential information
Text typed into an AI assistant is sent to the provider's servers for processing. Providers have different policies on how long they keep data and whether they use it to train models. Some plans let users switch off training on their data, and business plans usually carry stricter data terms than personal plans. Organisations should read the terms of the plan they actually use rather than assume.
Personal data and PDPA
If staff put customer data, such as names, phone numbers or purchase history, into an external AI tool, the organisation has to consider its obligations under Thailand's Personal Data Protection Act (PDPA), such as the legal basis for processing and the transfer of data to an outside processor. Consult the legal team or data protection officer before setting a policy.
Wrong answers being used
If a draft contract, tax information or a message to a customer contains an error from a hallucination and nobody checks it, the damage falls on the organisation, not on the AI provider.
Prompt injection
When an LLM is connected to email, websites or outside documents, the text in those sources may contain hidden instructions that try to make the AI do something the user did not intend. Systems that let AI send messages or change data on its own therefore need a step where a person approves the action.
Basic practice that works for most organisations: keep a list of approved tools, specify which types of data must not be entered, have a person review work before it is published, and record which work was done with AI help. Businesses that want to bring AI into marketing processes with controlled steps can see the Marketing Process Automation service.
What this means for Thai marketers
Thai users can now ask AI assistants in Thai, and many LLMs can answer in Thai, but there is far less Thai-language content on the web than English. The result is that when someone asks in Thai about a specialist topic, the system may have few good Thai sources to draw on. Businesses with clear, accurate Thai content that answers questions directly therefore have a better chance of being used. This is reasoning from how the mechanism works, not a guaranteed result.
Another factor is word segmentation. Thai has no spaces between words, so both search systems and models have to segment text themselves. Content that uses the terms people actually search for, and explains English terms alongside Thai ones, such as "llm" with "โมเดลภาษาขนาดใหญ่", helps readers and systems understand it the same way.
Finally, PDPA means that using customer data with AI tools needs a clear framework. Marketers who want to use LLMs to analyse customer data should start with anonymised data.
Frequently asked questions about LLMs
What is the difference between an LLM and ChatGPT?
An LLM is the language model itself, while ChatGPT is OpenAI's chat application that runs on the GPT model family. Think of an engine versus the whole car: the app adds other parts, such as the chat interface, web search and conversation memory.
Is an LLM intelligent, or does it really understand what it says?
An LLM works by computing statistical patterns in language and does not have human understanding or experience, even though it can solve problems and reason well on many tasks. Its output should be reviewed like a draft from an assistant.
How can I reduce hallucinations?
The most effective method is to have the model answer from documents you attach, or turn on web search and check the cited sources. Instruct the model to say it does not know when it lacks information, and always have a person check numbers, names and dates before use.
Does a small business need to do GEO yet?
Start with the basics that help SEO and GEO at the same time: content that answers customer questions directly, consistent business information across every channel, and a website search systems can access. This work is not wasted even if search behaviour changes more slowly than expected.
Can I put customer data into an AI assistant?
Not until the organisation has checked the service terms and has a PDPA-compliant policy in place. The safer route is to remove identifying information first, or use a business plan with clear data agreements.
Summary
An LLM is an AI model that learns language patterns from large amounts of text and builds answers by predicting the next piece step by step. It writes and summarises well, but it can be confidently wrong, so it needs human review and a data policy before use inside an organisation. On the marketing side, as more people ask AI, clear, verifiable content that AI systems can reach is worth more. If you want your brand ready for AI-driven search, see the GEO service page at Relevant Audience.
