JA EN

Prompt Engineering Practical Techniques - From Basics to Advanced Patterns

LLM Published: 2025年11月23日 Updated: 2026年01月04日
Prompt Engineering LLM Best Practices Chain-of-Thought

Introduction to Prompt Engineering

Prompt engineering is the practice of designing inputs to get desired outputs from LLMs. Good prompts dramatically improve result quality and consistency.

Basic Techniques

1. Zero-Shot Prompting

Direct instruction without examples:

Classify the sentiment of this text as positive, negative, or neutral:
Text: "The product exceeded my expectations"
Sentiment:

2. Few-Shot Prompting

Provide examples to guide the model:

Classify the sentiment:

Text: "Amazing quality!" → Positive
Text: "Terrible experience" → Negative
Text: "It's okay" → Neutral
Text: "Best purchase ever" →

3. Chain-of-Thought (CoT)

Ask the model to show reasoning:

Q: A store has 50 apples. They sell 20 and buy 15 more. How many?
A: Let's think step by step.
   - Start: 50 apples
   - Sell 20: 50 - 20 = 30
   - Buy 15: 30 + 15 = 45
   Final answer: 45

Advanced Patterns

System Prompts

Set the model’s role and behavior:

system_prompt = """You are an expert code reviewer. 
Focus on: security, performance, and maintainability.
Be concise and actionable."""

Structured Output

Request specific format:

Analyze this text and return JSON:
{
  "sentiment": "positive|negative|neutral",
  "confidence": 0.0-1.0,
  "key_phrases": ["phrase1", "phrase2"]
}

Self-Consistency

Generate multiple answers and vote:

answers = [llm.invoke(prompt) for _ in range(5)]
final_answer = most_common(answers)

Best Practices

PracticeDescription
Be SpecificClear instructions produce better results
Use DelimitersSeparate instructions, context, and input
Specify FormatTell the model how to structure output
Add ConstraintsSet boundaries (length, tone, style)
IterateTest and refine prompts

🛠 Key Tools

ToolPurposeLink
LangChainPrompt ManagementDetails
PromptLayerVersion ControlDetails
OpenAI PlaygroundTestingDetails

FAQ

Q1: What is the most important prompt engineering principle?

Be specific and clear. Vague prompts produce inconsistent results.

Q2: When should I use few-shot prompting?

Use when you need consistent output format or specific reasoning style.

Q3: What is Chain-of-Thought prompting?

A technique to improve reasoning by asking the model to show step-by-step thinking.

Summary

Effective prompt engineering requires clarity, examples, and iteration. Start with simple techniques and add complexity as needed.

📖 Related Articles

Share

Related Articles

Complete Guide to LLM Development Pitfalls - 7 Failure Patterns and Solutions
LLM 2025/11/21

Complete Guide to LLM Development Pitfalls - 7 Failure Patterns and Solutions

Implementing 'Autonomy' in AI Agents: 4 Agentic Workflow Design Patterns
AI Agents 2025/12/04

Implementing 'Autonomy' in AI Agents: 4 Agentic Workflow Design Patterns

AI Agent Development Pitfalls and Solutions - 2025 Edition
AI Agent 2025/11/27

AI Agent Development Pitfalls and Solutions - 2025 Edition

Recommended Articles

Implementing Self-Healing Infrastructure Architecture with Autonomous AI Agents

Implementing Self-Healing Infrastructure Architecture with Autonomous AI Agents

2026.03.18
AI Agent Error Handling Best Practices: Challenges and Solutions in Production

AI Agent Error Handling Best Practices: Challenges and Solutions in Production

2026.03.16
Beyond Stateless Agents: How Agentic Memory Enables 'Memory' and 'Learning'

Beyond Stateless Agents: How Agentic Memory Enables 'Memory' and 'Learning'

2026.03.02

Tag Cloud

#LLM (7) #ROI (7) #RAG (6) #AI Agents (5) #Python (5) #LangChain (4) #Best Practices (3) #Digital Transformation (3) #Enterprise AI (3) #Vector Database (3) #2025 (2) #2026 (2) #Agentic Workflow (2) #AI Agent (2) #AI ROI (2) #Anthropic (2) #Development (2) #Implementation Strategy (2) #LangGraph (2) #LlamaIndex (2) #Management Strategy (2) #MCP (2) #MIT Research (2) #Multi-Agent (2) #Agentic AI (1) #Agentic Memory (1) #Agentic RAG (1) #AI (1) #AI Agent Framework (1) #AI Coding Agents (1) #AI Engineering (1) #AutoGen (1) #Automation (1) #Autonomous Coding (1) #Backend Optimization (1) #Business Automation (1) #Business Value (1) #Business Value Assessment (1) #Chain-of-Thought (1) #Claude 3.5 Sonnet (1) #Computer Use (1) #CrewAI (1) #Cursor (1) #Design Pattern (1) #Development Efficiency (1) #Development Method (1) #Devin (1) #DX (1) #Entity Extraction (1) #Error Handling (1) #GitHub Copilot (1) #GraphRAG (1) #GUI Automation (1) #Information Retrieval (1) #Integration (1) #Knowledge Graph (1) #Kubernetes (1) #Milvus (1) #Model Context Protocol (1) #Multimodal RAG (1) #Neo4j (1) #Pinecone (1) #Pitfalls (1) #Prompt Engineering (1) #Qdrant (1) #Retrieval (1) #Similarity Search (1) #SLM (1) #SRE (1) #Strategy (1) #Troubleshooting (1) #Weaviate (1)