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What is a Large Language Model (LLM)? A Complete Beginner's Guide

· 8 min read
Kobkrit Viriyayudhakorn
Kobkrit Viriyayudhakorn
CEO @ iApp Technology

By Dr. Kobkrit Viriyayudhakorn, CEO & Founder, iApp Technology

You've probably heard of ChatGPT, Claude, or Google's Gemini. These are all powered by something called a Large Language Model or LLM. But what exactly is an LLM? How does it work? And why should you care?

In this beginner-friendly guide, we'll explain everything you need to know about LLMs in simple terms - no PhD required!

How Large Language Models Work

What is a Large Language Model (LLM)?​

A Large Language Model (LLM) is a type of artificial intelligence that can understand, generate, and work with human language. Think of it as a very sophisticated autocomplete system - but instead of just predicting the next word, it can write entire essays, answer complex questions, translate languages, and even write code.

Simple Analogy​

Imagine you have a friend who has read every book, article, and website on the internet. When you ask them a question, they can draw from all that knowledge to give you a thoughtful answer. That's essentially what an LLM does - except it's a computer program that has "learned" from billions of text documents.

5 Key Terms You Need to Know​

Before diving deeper, let's clarify some jargon that often confuses beginners:

1. Token​

A token is the basic unit that LLMs use to process text. It's not exactly a word - it's more like a chunk of text.

TextTokens
"Hello"1 token
"Hello world"2 tokens
"สวัสดีครับ" (Thai: Hello)3-4 tokens
"Artificial Intelligence"2 tokens

Why it matters: LLM pricing is usually based on tokens. More tokens = higher cost. Thai language typically uses more tokens than English for the same meaning.

2. Parameters​

Parameters are the "knowledge" stored inside an LLM. Think of them as the brain cells of the AI.

  • GPT-3: 175 billion parameters
  • GPT-4: ~1.7 trillion parameters
  • Chinda Thai LLM: 4 billion parameters
  • DeepSeek-V3: 685 billion parameters

Why it matters: More parameters generally mean better understanding and more nuanced responses, but also require more computing power.

3. Prompt​

A prompt is the input you give to an LLM - your question or instruction.

Good prompt: "Explain photosynthesis to a 10-year-old in 3 sentences."
Bad prompt: "Photosynthesis?"

Why it matters: The quality of your prompt directly affects the quality of the response. This skill is called "prompt engineering."

4. Context Window​

The context window is how much text an LLM can "remember" in a single conversation.

ModelContext Window
GPT-3.54,096 tokens
GPT-4128,000 tokens
Chinda Thai LLM40,960 tokens
DeepSeek-V3128,000 tokens

Why it matters: A larger context window means the AI can handle longer documents and remember more of your conversation.

5. Hallucination​

Hallucination is when an LLM generates information that sounds confident but is actually false or made up.

Example:

  • Question: "Who wrote the Thai novel 'The Lost Kingdom of Siam'?"
  • Hallucinated answer: "It was written by Kukrit Pramoj in 1965."
  • Truth: This novel doesn't exist!

Why it matters: Always verify important facts from LLM outputs, especially for critical decisions.

Why Are LLMs Important?​

LLMs are transforming how we work, learn, and communicate. Here's why they matter:

1. Democratizing AI​

Previously, using AI required extensive technical knowledge. Now, anyone can interact with AI through natural language.

2. Boosting Productivity​

Tasks that took hours can now be done in minutes:

  • Writing emails and reports
  • Summarizing long documents
  • Translating between languages
  • Generating code

3. Breaking Language Barriers​

LLMs can translate and understand multiple languages, making information more accessible globally - including Thai!

4. Enabling Innovation​

From customer service chatbots to medical diagnosis assistance, LLMs are enabling new applications across every industry.

What Problems Do LLMs Solve?​

LLM Applications in Thailand and Business

Content Creation​

  • Writing marketing copy, blog posts, and social media content
  • Generating product descriptions
  • Creating educational materials

Customer Service​

  • 24/7 automated support chatbots
  • Instant responses to common questions
  • Multilingual customer support

Data Analysis​

  • Summarizing large documents
  • Extracting insights from text data
  • Sentiment analysis of customer feedback
  • Reviewing contracts
  • Answering legal questions
  • Explaining regulations in plain language

Code Development​

  • Writing and debugging code
  • Explaining programming concepts
  • Converting code between languages

Translation​

  • Real-time language translation
  • Localizing content for different markets
  • Maintaining context and tone across languages

How Do LLMs Work?​

Let's break down the magic behind LLMs in simple terms:

Step 1: Training (Learning Phase)​

The LLM reads billions of text documents from books, websites, and articles. During this process, it learns:

  • Grammar and language structure
  • Facts and knowledge
  • Patterns in how humans communicate
  • Context and relationships between concepts

Step 2: Understanding Patterns​

Instead of memorizing text, the LLM learns statistical patterns. For example:

  • After "The capital of Thailand is...", the word "Bangkok" is highly likely
  • After "Good morning," a greeting response is expected
  • Technical questions expect technical answers

Step 3: Generating Responses​

When you give a prompt, the LLM:

  1. Converts your text into tokens
  2. Processes tokens through billions of parameters
  3. Predicts the most likely next tokens
  4. Generates a coherent response

The Neural Network Architecture​

Modern LLMs use a technology called Transformers (introduced by Google in 2017). Key components include:

  • Attention Mechanism: Helps the model focus on relevant parts of the input
  • Layers: Multiple processing layers that refine understanding
  • Training Data: Quality and quantity of data affects model performance

How to Use LLMs​

Method 1: Chat Interfaces​

The easiest way is through chat interfaces like ChatGPT, Claude, or iApp's AI services.

Method 2: API Integration​

For developers, LLMs are available via APIs. Here's an example using iApp's Chinda Thai LLM:

import requests

response = requests.post(
    "https://api.iapp.co.th/v3/llm/chinda-thaillm-4b/chat/completions",
    headers={"apikey": "YOUR_API_KEY"},
    json={
        "model": "chinda-qwen3-4b",
        "messages": [
            {"role": "user", "content": "อธิบายเกี่ยวกับ AI ให้หน่อย"}
        ],
        "max_tokens": 4096
    }
)

print(response.json()["choices"][0]["message"]["content"])

Method 3: Local Deployment​

Some open-source LLMs like Chinda can be run on your own computer for privacy and cost savings.

LLM Examples in Thai Context​

Need legal advice? iApp's Thanoy is an LLM specialized in Thai law:

User: โดนโจรตีหัว ผิดมาตราอะไรครับ
Thanoy: การถูกทำร้ายร่างกายเช่นนี้ อาจเข้าข่ายความผิดตามประมวลกฎหมายอาญา...

Thanoy has been trained on over 10,000 Thai legal articles and can answer legal questions in seconds.

Example 2: Thai Language Understanding (Chinda)​

Chinda is Thailand's first open-source Thai LLM, optimized for Thai language understanding:

User: สรุปข่าวนี้ให้หน่อย: [ข้อความข่าวยาวๆ]
Chinda: ข่าวนี้พูดถึง... [สรุปกระชับ]

Example 3: Advanced Reasoning (DeepSeek-V3)​

For complex tasks requiring deep reasoning:

User: Help me solve this math problem step by step...
DeepSeek: Let me think through this carefully...
[Shows detailed reasoning process]

Real-World Thai Use Case: Disaster Relief​

During the 2024 Southern Thailand floods, Chinda LLM was used to automatically parse thousands of emergency requests from social media:

Input (messy social media post):

ขอความช่วยเหลือ คุณแม่ติดอยู่ในบ้าน น้ำเข้าบ้านประมาณเอว
มีโรคประจำตัวเบาหวาน โทร. 081-234-5678

Output (structured data):

{
  "location": "บ้านในพื้นที่น้ำท่วม",
  "situation": "น้ำท่วมระดับเอว",
  "medical_condition": "โรคเบาหวาน",
  "contact": "081-234-5678",
  "urgency": "สูง"
}

This automation helped relief coordinators prioritize and respond faster!

iApp Technology's LLM Services​

At iApp Technology, we offer several LLM solutions tailored for Thai businesses:

Chinda Thai LLM 4B​

  • FREE until December 31, 2025
  • Optimized for Thai language
  • Open-source and can run locally
  • Try Chinda Demo
  • Specialized in Thai law
  • Access to 10,000+ legal articles
  • Fast 15-second responses
  • Try Thanoy Demo

DeepSeek-V3.2​

  • 685 billion parameters
  • Advanced reasoning capabilities
  • Competitive pricing (~10 THB/1M input tokens)
  • Try DeepSeek Demo

Getting Started with iApp LLM APIs​

Step 1: Create a Free Account​

Visit iapp.co.th/register to create your account.

Step 2: Get Your API Key​

Go to API Key Management to generate your key.

Step 3: Make Your First API Call​

curl -X POST "https://api.iapp.co.th/v3/llm/chinda-thaillm-4b/chat/completions" \
  -H "apikey: YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "chinda-qwen3-4b",
    "messages": [{"role": "user", "content": "สวัสดีครับ"}]
  }'

Step 4: Explore More​

Check out our full documentation and join our Discord community.

Summary​

Large Language Models are transforming how we interact with technology. Here's what we covered:

  • LLMs are AI systems that understand and generate human language
  • Key terms: Tokens, Parameters, Prompts, Context Window, Hallucination
  • Applications: Content creation, customer service, data analysis, legal AI, coding
  • Thai solutions: Chinda (Thai LLM), Thanoy (Legal AI), DeepSeek (Advanced reasoning)

The AI revolution is here, and Thai businesses can leverage these powerful tools through iApp Technology's APIs.

Ready to Try LLMs?​

Start exploring the power of Large Language Models today:

Have questions? Join our Discord community or email us at support@iapp.co.th.

iApp Technology Co., Ltd. Thailand's Leading AI Company