Large Language Models (LLM)

What are LLMs?

One-Line Explanation

LLM = Large Language Model

AI trained on massive amounts of text data
Can understand and generate human language

Analogy:
- Regular person: Read 100 books
- LLM: "Read" the entire internet

Result:
- Regular person: Can answer various questions
- LLM: Can do more, handle complex tasks

How LLMs Work

1. Training Stage: Learn Language Rules

Input: Entire internet's text (trillions of tokens)

Training goal:
Given previous words, predict the next word

Example:
Input: "Today's weather is"
Output: "great" (learned from "Today's weather is great...")

After repeating trillions of times:
AI learns language rules
Learns knowledge
Learns reasoning

2. Application Stage: Generate Responses

User input: "What is blockchain?"

AI thought process:
1. Understand question: "User wants blockchain definition"
2. Retrieve knowledge: "Blockchain is..."
3. Organize language: Explain in accessible way
4. Generate response: "Blockchain is a type of..."

LLM Core Concepts

Parameters

Parameters = AI's "brain" neural connections

Common models comparison:
| Model | Parameters | Description |
|-------|------------|-------------|
| GPT-3 | 175 billion | Early large model |
| GPT-4 | ~1.8 trillion | Multimodal |
| Claude 3 | Not disclosed | Strong at long text |
| Gemini | ~1.5 trillion | Google's model |

Analogy:
- Human brain: ~100 billion neurons
- GPT-3: 175 billion parameters

Tokens

Token = Smallest unit AI processes text

Example:
"hello world" 
→ ["hello", " world"]
→ 2 tokens

Chinese characters:
"你好世界"
→ Might be 4 tokens (one character = one token)

Estimate:
1000 tokens ≈ 750 English words
1000 tokens ≈ 400-500 Chinese characters

Context Window

Context window = How much AI can "remember"

Example:
- GPT-3.5: 4K tokens
- GPT-4: 128K tokens
- Claude 3: 200K tokens

Analogy:
Context = AI's "short-term memory"
Exceed and it forgets

How to Call LLMs?

Traditional Method

1. OpenAI: api.openai.com
   - Register account
   - Get API Key
   - Call API
   - Pay per token

2. Anthropic: api.anthropic.com
   - Similar process
   - Claude API

Pain Points

❌ Need to manage multiple accounts
❌ Different billing per platform
❌ Non-unified API format
❌ Scattered bills, hard to manage

Solution: AI Gateway

PulsePay AI Gateway

Unified entry, one account:

✅ Access multiple AI models
   - OpenAI GPT-4
   - Anthropic Claude
   - Google Gemini

✅ Unified billing
   - Pay with USDT/BNB
   - Manage one bill

✅ Usage statistics
   - Clear understanding of usage
   - Cost analysis support

Website: ai.pulsepay.fun

Next Steps

• AI Agent — AI autonomously executes tasks
• AI Pricing — Understand AI cost structure