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# Concept: System Prompts & Agent Specialization
## Overview
This example demonstrates how to transform a general-purpose LLM into a **specialized agent** using **system prompts**. The key insight: you don't need different models for different tasks—you need different instructions.
## What is a System Prompt?
A **system prompt** is a persistent instruction that shapes the AI's behavior for an entire conversation session.
### Analogy
Think of hiring someone for a job:
```
Without System Prompt With System Prompt
───────────────────── ──────────────────────
"Hi, I'm an AI." "I'm a professional translator
with expertise in scientific
What do you want?" German. I follow strict quality
guidelines and output format."
```
## How System Prompts Work
### The Context Structure
```
┌─────────────────────────────────────────────┐
│ CONTEXT WINDOW │
│ │
│ ┌───────────────────────────────────────┐ │
│ │ SYSTEM PROMPT (Always present) │ │
│ │ "You are a professional translator..." │
│ │ "Follow these rules..." │ │
│ └───────────────────────────────────────┘ │
│ ↓ │
│ ┌───────────────────────────────────────┐ │
│ │ USER MESSAGES │ │
│ │ "Translate this text..." │ │
│ └───────────────────────────────────────┘ │
│ ↓ │
│ ┌───────────────────────────────────────┐ │
│ │ AI RESPONSES │ │
│ │ (Shaped by system prompt) │ │
│ └───────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
```
The system prompt sits at the top of the context and influences **every** response.
## Agent Specialization Pattern
### Transformation Flow
```
┌──────────────────┐ ┌─────────────────┐ ┌──────────────────┐
│ General Model │ + │ System Prompt │ = │ Specialized Agent│
│ │ │ │ │ │
│ • Knows many │ │ • Define role │ │ • Translation │
│ things │ │ • Set rules │ │ Agent │
│ • No specific │ │ • Constrain │ │ • Coding Agent │
│ role │ │ output │ │ • Analysis Agent │
└──────────────────┘ └─────────────────┘ └──────────────────┘
```
### Example Specializations
**Translation Agent (this example):**
```
System Prompt = Role + Rules + Output Format
```
**Code Assistant:**
```javascript
systemPrompt: "You are an expert programmer.
Always provide working code with comments.
Explain complex logic."
```
**Data Analyst:**
```javascript
systemPrompt: "You are a data analyst.
Always show your calculations step-by-step.
Cite data sources when available."
```
## Anatomy of an Effective System Prompt
### The 5 Components
```
┌─────────────────────────────────────────┐
│ 1. ROLE DEFINITION │
│ "You are a [specific role]..." │
├─────────────────────────────────────────┤
│ 2. TASK DESCRIPTION │
│ "Your goal is to..." │
├─────────────────────────────────────────┤
│ 3. BEHAVIORAL RULES │
│ "Always do X, Never do Y..." │
├─────────────────────────────────────────┤
│ 4. OUTPUT FORMAT │
│ "Format your response as..." │
├─────────────────────────────────────────┤
│ 5. CONSTRAINTS │
│ "Do NOT include..." │
└─────────────────────────────────────────┘
```
### This Example's Structure
```
Role: "Professional scientific translator"
Task: "Translate English to German with precision"
Rules: 8 specific translation guidelines
Format: Idiomatic German, scientific style
Constraints: "ONLY translated text, no explanation"
```
## Why Detailed System Prompts Matter
### Comparison Study
**Minimal System Prompt:**
```javascript
systemPrompt: "Translate to German"
```
**Result:**
- May add unnecessary explanations
- Inconsistent terminology
- Mixed formality levels
- Extra conversational text
**Detailed System Prompt (this example):**
```javascript
systemPrompt: `You are a professional translator...
- Rule 1: Preserve technical accuracy
- Rule 2: Use idiomatic German
- Rule 3: Follow scientific conventions
...
DO NOT add any explanations`
```
**Result:**
- ✅ Consistent quality
- ✅ Correct terminology
- ✅ Proper formatting
- ✅ Only translation output
### Quality Impact
```
Detail Level Output Quality
─────────── ─────────────────
Very minimal → Unpredictable
Basic role → Somewhat consistent
Detailed → Highly consistent ⭐
Over-detailed → May confuse model
```
## System Prompt Design Patterns
### Pattern 1: Role-Playing
```
"You are a [profession] with expertise in [domain]..."
```
Makes the model adopt that perspective.
### Pattern 2: Rule-Based
```
"Follow these rules:
1. Always...
2. Never...
3. When X, do Y..."
```
Explicit constraints lead to predictable behavior.
### Pattern 3: Output Formatting
```
"Format your response as:
- JSON
- Markdown
- Plain text only
- Step-by-step list"
```
Controls the structure of responses.
### Pattern 4: Contextual Awareness
```
"You remember: [previous facts]
You know that: [domain knowledge]
Current situation: [context]"
```
Primes the model with relevant information.
## How This Relates to AI Agents
### Agent = Model + System Prompt + Tools
```
┌────────────────────────────────────────────┐
│ AI Agent │
│ │
│ ┌──────────────────────────────────────┐ │
│ │ System Prompt (Agent's "Identity") │ │
│ └──────────────────────────────────────┘ │
│ ↓ │
│ ┌──────────────────────────────────────┐ │
│ │ LLM (Agent's "Brain") │ │
│ └──────────────────────────────────────┘ │
│ ↓ │
│ ┌──────────────────────────────────────┐ │
│ │ Tools (Agent's "Hands") [Optional] │ │
│ └──────────────────────────────────────┘ │
└────────────────────────────────────────────┘
```
**In this example:**
- System Prompt: "You are a translator..."
- LLM: Apertus-8B model
- Tools: None (translation is done by the model itself)
**In more complex agents:**
- System Prompt: "You are a research assistant..."
- LLM: Any model
- Tools: Web search, calculator, file access, etc.
## Practical Applications
### 1. Domain Specialization
```
Medical → "You are a medical professional..."
Legal → "You are a legal expert..."
Technical → "You are an engineer..."
```
### 2. Output Control
```
JSON API → "Always respond in valid JSON"
Markdown → "Format all responses as markdown"
Code → "Only output executable code"
```
### 3. Behavioral Constraints
```
Concise → "Use maximum 2 sentences"
Detailed → "Explain thoroughly with examples"
Neutral → "Avoid opinions, state only facts"
```
### 4. Multi-Language Support
```
systemPrompt: `You are a multilingual assistant.
Respond in the same language as the input.`
```
## Chat Wrappers Explained
Different models need different conversation formats:
```
Model Type Format Needed Wrapper
────────────── ─────────────────── ─────────────────
Llama 2/3 Llama format LlamaChatWrapper
GPT-style ChatML format ChatMLWrapper
Harmony models Harmony format HarmonyChatWrapper
```
**What they do:**
```
Your Message → [Chat Wrapper] → Formatted Prompt → Model
↓
Adds special tokens:
<|system|>, <|user|>, <|assistant|>
```
The wrapper ensures the model understands which part is the system prompt, which is the user message, etc.
## Key Takeaways
1. **System prompts are powerful**: They fundamentally change how the model behaves
2. **Detailed is better**: More specific instructions = more consistent results
3. **Structure matters**: Role + Rules + Format + Constraints
4. **No retraining needed**: Same model, different behaviors
5. **Foundation for agents**: System prompts are the first step in building specialized agents
## Evolution Path
```
1. Basic Prompting (intro.js)
↓
2. System Prompts (translation.js) ← You are here
↓
3. System Prompts + Tools (simple-agent.js)
↓
4. Multi-turn reasoning (react-agent.js)
↓
5. Full Agent Systems
```
This example bridges the gap between basic LLM usage and true agent behavior by showing how to specialize through instructions.
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