Not Using Claude Well Wastes Money? 10 Advanced Tips to 3x Your Efficiency

Introduction

When I first used Claude six months ago, honestly, I was pretty disappointed. Felt like just regular conversation—didn’t seem that different from ChatGPT. Seeing colleagues use Claude to create cool interactive tools that could remember entire project contexts, I was genuinely confused: Why is my Claude just a “regular chatbot”?

Later I realized the problem wasn’t Claude—it was me. I had no idea Claude had so many hidden features—Artifacts, Projects, Extended Thinking… These features completely transformed how I work. Looking back now, that half year was basically “wasting” Claude’s capabilities.

In this article I want to share 10 practical techniques, plus over 20 ready-to-copy Prompt templates. If you’re also using Claude but always feel “it could be better,” this article is for you. Estimated 15-minute read, immediately applicable after finishing. I dare say your work efficiency will improve at least 3x.

Part One: Claude vs ChatGPT - Did You Really Choose Right?

Tip 1: Understand Claude’s 3 Core Advantages

Honestly, at first I couldn’t tell the difference between Claude and ChatGPT either. Not until I tried having them both analyze a 200-page technical document did the difference suddenly become clear.

Claude’s Three Killer Features:

1. 200K Ultra-Long Context Window What does this number mean? About 500 pages of text. Once I threw an entire project codebase (about 300 files) at Claude—it could actually accurately understand relationships between different modules. ChatGPT’s 128K, while not small, genuinely struggles with super-large projects.

2. More Natural Writing Quality This is a bit hard to quantify, but Claude’s text really feels more human-written. Anthropic used “Constitutional AI” training methods, teaching Claude more nuanced language expression. I once had it revise a technical blog post—the revised version even surprised me. Completely no “AI flavor.”

3. Stronger Code Ability and Security TELUS company’s 57,000 employees use Claude, creating over $90 million in business value. Zapier team says their automation tasks grew 10x because of Claude. These numbers aren’t just hype—Claude genuinely performs excellently in enterprise applications.

My Usage Recommendations:

Long document analysis, code projects → Use Claude
Casual chat, need memory features → Use ChatGPT
Creative brainstorming → Try both

Don’t misunderstand—I’m not saying ChatGPT is bad. Just in different scenarios, you really should use the right tool.

Part Two: Basic Technique Upgrades

Tip 2: System Prompts - Give Claude a “Persona”

I really strongly recommend this feature! It’s like putting a professional “mask” on Claude, keeping it in a certain professional state at all times.

What Are System Prompts? Simply put, it’s telling Claude before each conversation: “Who you are, how to respond, what style to use.” Then you don’t have to repeat background every time.

3 Setup Methods:

1. Projects (most common for me): Add Custom Instructions in Project settings
2. Pre-Chat: Send an instruction before each conversation starts
3. API System Message: If using API, can set system message

5 Scenario Templates, copy directly to use:

Template 1: Code Review Expert

You are a senior code review expert, focused on:
- Discovering potential bugs and performance issues
- Providing specific optimization suggestions (not generic advice)
- Evaluating code maintainability and scalability
- Giving 1-10 score and explaining reasoning

Response style: Professional but friendly, use specific examples to illustrate issues
Output format:
1. Overall score
2. Main issues list
3. Optimization suggestions (with code examples)
4. Priority labels

Template 2: Technical Documentation Writing Assistant

You are a technical documentation expert helping me create high-quality technical articles. Requirements:
- Explain complex concepts in plain language (assume readers are smart beginners)
- Every technical point includes practical examples
- Clear structure: Outline → Detailed explanation → Summary → Practice
- Avoid jargon dumping, use analogies when necessary

Tone: 60% professional + 40% conversational
Target readers: Developers with 2-3 years experience

Template 3: Product Requirements Analyst

You are a product requirements analyst, responsible for:
- Transforming vague ideas into clear requirement documents
- Identifying potential edge cases and risks
- Evaluating implementation difficulty and priorities
- Providing technical solution suggestions

Output format:
[Requirements Overview]
[User Scenarios] (3-5 specific scenarios)
[Feature List] (prioritized)
[Technical Challenges]
[Risk Assessment]

Template 4: Data Analysis Consultant

You are a data analysis consultant, specializing in:
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA)
- Visualization solution design
- Insight extraction and decision recommendations

Analysis workflow:
1. Data overview (shape, types, missing values)
2. Key findings (speak with numbers)
3. Visualization recommendations (specific chart types)
4. Action recommendations (at least 3)

Responses must include runnable Python code

Template 5: Content Creation Editor

You are a content editor, responsible for optimizing articles:
- Remove AI flavor (avoid "therefore," "in summary," etc.)
- Enhance readability (short sentences, paragraphs 3-4 lines)
- Integrate real emotion and personal touches
- SEO optimization (naturally incorporate keywords)

Editing principles:
- Preserve author's unique voice
- Replace formal language with "actually," "honestly"
- Use more specific examples, fewer abstract concepts
- Each paragraph expresses one core idea

Effect Comparison:

• No system prompt: Every time must explain “I need professional code review,” responses often go off-topic
• After setup: Claude automatically enters “expert mode,” responses hit the mark, format consistent

This feature literally doubled my work efficiency. Strongly recommend spending 5 minutes to set it up.

Tip 3: XML Tags - Secret Weapon for Claude Understanding Complex Tasks

This technique took my Claude usage experience to another level.

Why Use XML Tags? Ever encounter this: task gets complex, Claude starts “misunderstanding”? XML tags solve this problem. They structure information so Claude clearly knows which part is background, which is task, which are constraints.

Common Tags:

• <context> - Background information
• <task> - Specific task
• <examples> - Examples
• <constraints> - Constraints
• <output_format> - Output format

3 Practical Templates:

Template 1: Code Refactoring Request

<context>
This is a user authentication module currently using JWT tokens, but has performance issues.
Codebase: Python FastAPI
Current problem: Token validation takes too long, affecting API response speed
</context>

<task>
Refactor token validation logic in auth.py, requirements:
1. Optimize validation speed (target: reduce time cost by 50%)
2. Maintain unchanged security
3. Add caching mechanism
</task>

<constraints>
- Cannot change API interface
- Must be backward compatible with old tokens
- Code style follows PEP 8
</constraints>

<output_format>
1. Complete refactored code
2. Performance comparison analysis
3. Test cases
</output_format>

Template 2: Document Analysis

<context>
This is a product requirements document involving new payment functionality.
Target users: E-commerce platform merchants
Tech stack: React + Node.js
</context>

<task>
Analyze this requirements document, identify:
- Technical implementation challenges
- Potential security risks
- Edge cases
- Development time estimation
</task>

<examples>
Edge case examples:
- User disconnects during payment
- Concurrent payment conflicts
- Refund process anomalies
</examples>

<output_format>
[Technical Challenges] (rate difficulty 1-5)
[Security Risks] (label level High/Medium/Low)
[Edge Cases Checklist]
[Development Time Estimate] (Optimistic/Pessimistic/Most Likely)
</output_format>

Template 3: Multi-Step Task

<task>
Help me complete the full technical blog creation process:

Step 1: Topic Analysis
- Analyze "React Hooks Best Practices" topic
- Identify target audience pain points
- List 3-5 core points

Step 2: Outline Design
- Create article outline (including intro, 3-4 main sections, conclusion)
- Each section clarifies output value

Step 3: Content Creation
- Write complete draft based on outline
- Integrate code examples
- Control word count to 2000-2500 words

Step 4: Optimization
- Remove AI flavor
- SEO optimization
- Readability improvement
</task>

<constraints>
- Target readers: Developers with 1-2 years React experience
- Tone: Professional + conversational
- Must include runnable code examples
</constraints>

Effect Comparison:

• No structure: “Help me analyze this requirements doc” → Claude might miss key points
• XML structured: Clear labeled requirements → Claude’s response is comprehensive and accurate

This technique is especially suitable for complex tasks. Try it—you’ll love this precise control feeling.

Tip 4: Few-shot Examples - Teaching Claude to “Learn by Example”

Just like teaching kids—giving a few examples works best.

What Is Few-shot Learning? Simply put, show Claude a few “example answers” so it mimics the style and format you want. Particularly suitable for formatted tasks like data extraction, code format unification, writing style imitation.

Golden Rule: 3-5 examples optimal—too few won’t teach, too many causes confusion.

3 Application Scenarios:

Scenario 1: Unify Code Format

Task: Unify all function comments to Google style

Example 1:
Input:
def calculate(a, b):
    # Calculate sum of two numbers
    return a + b

Output:
def calculate(a, b):
    """Calculate sum of two numbers

    Args:
        a (int): First addend
        b (int): Second addend

    Returns:
        int: Sum of two numbers
    """
    return a + b

Example 2:
Input:
def get_user(id):
    # Get user info by ID
    return db.query(id)

Output:
def get_user(id):
    """Get user information by ID

    Args:
        id (str): Unique user identifier

    Returns:
        dict: User info dictionary containing name, email etc.
        None: If user doesn't exist
    """
    return db.query(id)

Now please process the following function:
[your code]

Scenario 2: Writing Style Imitation

Task: Rewrite technical explanations in conversational style

Example 1:
Input: API call frequency is limited, exceeding threshold will return 429 error.
Output: Note, you can't call the API unlimited times. If you request too frequently, you'll get a 429 error—meaning "slow down, let the server rest."

Example 2:
Input: Use JWT token for identity authentication, token valid for 24 hours.
Output: After login you'll get a JWT token—treat it as your "access pass." This pass is valid for 24 hours, after expiration need to log in again.

Example 3:
Input: Database connection uses connection pooling, max 100 connections.
Output: To boost performance, we use connection pooling. Think of it as a "connection warehouse" with 100 connections ready. Use one, return it, next time grab directly without recreating.

Now please rewrite:
[your technical text]

Scenario 3: Data Extraction Task

Task: Extract structured information from product descriptions

Example 1:
Input: iPhone 15 Pro, 256GB storage, titanium material, supports 5G, price 8999 yuan
Output:
{
  "product": "iPhone 15 Pro",
  "storage": "256GB",
  "material": "Titanium",
  "connectivity": "5G",
  "price": 8999
}

Example 2:
Input: MacBook Air M2, 8-core CPU, 13.6-inch Retina display, midnight color, starting 9499 yuan
Output:
{
  "product": "MacBook Air M2",
  "cpu": "8-core",
  "screen": "13.6-inch Retina",
  "color": "Midnight",
  "price": 9499
}

Now please extract:
[your product description]

My Experience: Quality over quantity. 3 high-quality examples > 10 random ones. Plus examples should cover different cases (simple, complex, edge cases).

Tip 5: Extended Thinking - Let Claude “Think Deeply”

Sometimes slow is fast—letting Claude think more really makes a difference.

What Is Extended Thinking? This is a Claude 4 new feature that can give Claude up to 32K tokens of “thinking budget” (about 24,000 words of thinking process). It deeply thinks before answering—you can see the entire reasoning process.

Applicable Scenarios:

• Complex code bug debugging
• Multi-round reasoning problems (like “Why is this algorithm slow?”)
• Strategic planning analysis
• Decisions weighing multiple factors

How to Trigger:

1. Direct request: “Please think through this problem step by step”
2. Enable Extended Thinking mode (in settings)

2 Practical Cases:

Case 1: Complex Bug Debugging

<task>
This code has a hard-to-reproduce bug—users report data inconsistency occasionally under high concurrency.
Please use Extended Thinking to deeply analyze possible causes.

[paste code]

Requirements:
- Analyze each possible cause step by step
- Consider concurrency, locking mechanisms, database transactions, etc.
- Give 3 most likely causes and solutions
</task>

Case 2: Business Strategy Analysis

<task>
Our company is considering entering the AI tools market. Please use Extended Thinking to analyze:
1. Market opportunity (TAM, competitive landscape)
2. Our strengths and weaknesses
3. Risk factors
4. Entry strategy recommendations

Background:
- We're a B2B SaaS company with 100K enterprise users
- Current product is project management tool
- Tech team 50 people, 5 have AI experience
- Budget $5M

Please think deeply then provide complete strategic analysis report.
</task>

Advanced Technique:

Adjust Thinking Budget:

Please use maximum thinking budget (32K tokens) to analyze this system architecture design,
focus on:
- Scalability (future 10x user growth)
- Maintainability (can newcomers quickly onboard)
- Cost (server, database, CDN cost estimate)
- Risk (single point failure, data loss risks)

[system architecture diagram]

View Thinking Process: After enabling Extended Thinking, Claude will show thinking steps. You can see how it reasons step by step—very helpful for learning and debugging.

Combo Use: Extended Thinking + Projects works best. Claude can deeply think based on project knowledge base.

Effect Comparison:

• Normal mode: Quick answers, but might miss details
• Extended Thinking: Deep reasoning, considers multiple angles, more reliable

This feature is like installing a “deep thinking mode” for Claude. When facing complex problems, don’t rush for answers—let it think slowly.

Part Three: Advanced Feature Deep Dive

Tip 6: Artifacts - Create Interactive Tools in 3 Minutes

The first time I used Artifacts, I was genuinely amazed.

What Are Artifacts? Simply put, it’s “independent content” Claude creates—can be interactive web pages, code tools, data visualizations, even mini-games. Users globally have created over 500 million Artifacts—this number shows it’s genuinely useful.

2025 New Features:

• MCP integration (can connect GitHub, Notion, etc.)
• Persistent storage (your Artifacts won’t get lost)
• Stronger interactivity

Supported Types:

• Interactive web pages (HTML/CSS/JavaScript)
• Data visualization (charts, dashboards)
• Code tools (calculators, converters, generators)
• Documents and tutorials (Markdown format)

3 Practical Cases:

Case 1: Data Visualization Dashboard

Please create an Artifact displaying the following sales data:

Data:
2024 Q1: 1.2M
2024 Q2: 1.5M
2024 Q3: 1.8M
2024 Q4: 2.1M

Requirements:
- Type: Interactive bar chart
- Function: Hover shows specific values
- Style: Modern minimalist, use blue gradient
- Extra display: Year-over-year growth rate

Case 2: Interactive Tutorial

Create an Artifact teaching beginners how to use basic Git commands.

Requirements:
- Format: Step-by-step tutorial
- Each step has code examples
- Include common errors and solutions
- Add "Try it" buttons simulating command execution results
- Style: Friendly, no jargon

Case 3: Practical Tool - JSON Formatter

Create a JSON formatting tool Artifact:

Features:
- Input: Unformatted JSON string
- Processing: Auto format, validate syntax, highlight
- Output: Beautified JSON + error hints
- Extra: Provide both "compress" and "beautify" modes

Interaction:
- Real-time formatting (immediate feedback while typing)
- One-click copy formatted result
- Error line highlighting

Style:
- Dark theme
- Code highlighting (different colors for keys, values, numbers)
- Responsive design (works well on mobile)

Complete Operation Flow:

1. Describe tool you want in natural language
2. Claude auto-generates Artifact
3. Preview effect on right side
4. If not satisfied, directly say “change color to green” or “add search function”
5. When satisfied, share link or export code

My Usage Tips:

• Specify interaction clearly: Don’t just say “make a chart,” say “clicking can filter data”
• Describe style specifically: Don’t say “nice design,” say “flat style, use #4A90E2 blue”
• Give real data: Testing with actual data gives more accurate results

Artifacts is genuinely one of my most-used features now. Demo tools that used to take half a day, now done in 3 minutes.

Tip 7: Projects - Say Goodbye to Repeating Background

Since using Projects, I never have to explain project background every time anymore.

What Are Projects? Think of it as a “knowledge base” prepared for Claude. You upload project-related code, docs, data—Claude automatically remembers. After that, every conversation, it can draw on this background info without you repeating.

Core Features:

• 200K context capacity (about 500 pages text)
• Team collaboration (can set different member permissions)
• Combined with Artifacts (create tools based on Project knowledge)

4-Step Setup Process:

Step 1: Create Project

• Click “Projects” button
• Name it (like “E-commerce Backend Development”)
• Select team members (if team project)

Step 2: Upload Background Materials I usually upload:

• Codebase (main files, not everything)
• Technical docs (API docs, architecture diagrams)
• Data dictionary
• Brand guidelines (if content project)
• Previous conversation records

Step 3: Set Custom Instructions

Project: E-commerce platform backend development
Tech stack: Python FastAPI + PostgreSQL + Redis
Code standards: PEP 8 + Google-style comments
Testing requirements: Unit test coverage >80%

Your role: Backend development expert
Response style: Direct runnable code with brief explanation

Step 4: Start Conversation Now you can directly ask: “How to optimize user module authentication logic?” Claude will automatically draw on uploaded code and docs, giving targeted suggestions.

5 Application Scenarios:

Scenario 1: Project Development Upload content:

• Core code in src/ directory
• README.md
• Technical architecture docs
• API design docs

Effect: Claude understands your entire project structure, can analyze cross-file issues.

Scenario 2: Content Creation Upload content:

• Brand guidelines
• Previous article samples
• Target audience profiles
• SEO keyword list

Effect: Content Claude generates automatically fits your brand tone and style.

Scenario 3: Learning Research Upload content:

• Course materials
• Paper PDFs
• Notes
• Practice problems

Effect: Claude becomes your “personal tutor,” answering questions based on course content.

Scenario 4: Data Analysis Upload content:

• Dataset CSV
• Data dictionary
• Analysis framework docs
• Previous analysis reports

Effect: Claude understands your data structure, quickly generates analysis code.

Scenario 5: Team Collaboration Upload content:

• Team standards docs
• Project roadmap
• Meeting minutes
• Decision logs

Effect: All team members conversing with Claude based on same background knowledge.

Best Practices:

• Clear document naming: Architecture-Design.md better than doc1.md
• Regular updates: Update knowledge base promptly when project has major changes
• Control file size: Upload core content only, not more is better
• Multi-Project management: Different projects separate, don’t mix

I now have 5 active Projects: company project, personal blog, learning Python, data analysis, writing assistant. Each Project like a “dedicated Claude.”

Tip 8: MCP Integration - Connect Your Workflow

MCP truly integrated Claude into my workflow.

What Is MCP (Model Context Protocol)? Simply put, it lets Claude connect to tools you use daily—GitHub, Notion, Slack, Jira, etc. Claude can directly read and operate data in these tools.

Connectable Tools (partial list):

• GitHub (code review, issue management)
• Notion (document read/write)
• Slack (message handling)
• Google Drive (file access)
• Jira (task management)
• Calendar (schedule arrangement)

3 Practical Cases:

Case 1: GitHub Integration - Code Review Automation

Access GitHub repository mycompany/backend via MCP,
analyze last 10 Pull Requests, identify:
- Common code issues
- Whether code review suggestions are adopted
- Which developers need more code quality training

Generate team code quality report.

Case 2: Notion Integration - Auto-Generate Docs

Access Notion database "Product Requirements" via MCP,
organize this week's 5 new requirements into:
- Technical feasibility analysis doc
- Development task breakdown (save to Jira)
- Time estimate table

Auto-create under Notion's "Development Docs" page.

Case 3: Slack Integration - Intelligent Message Handling

Monitor Slack #support channel via MCP,
when users ask questions:
1. Search historical solutions
2. If similar issue found, directly reply with solution
3. If new issue, @relevant tech person and summarize question points

Generate daily support issue summary report.

Configuration Steps (GitHub example):

1. Install MCP Server (follow official docs)
2. Configure GitHub token
3. Authorize MCP connection in Claude
4. Test: List all my repositories via MCP

My Usage Insights: MCP’s real value isn’t “controlling tools” but “connecting workflows.” For example, my current writing workflow:

1. Claude writes article based on Projects knowledge base
2. Auto-save to Notion via MCP
3. Create Jira tasks via MCP: layout, images, publish
4. Send Slack notification via MCP: draft complete

Entire process automated—I only focus on content creation.

Tip 9: Prompt Chaining Techniques - Combo Doubles Efficiency

After mastering this technique, feels like Claude became my dedicated team.

What Is Prompt Chaining? Breaking complex tasks into multiple steps, each step using specialized Prompt, results passing layer by layer. Like a factory assembly line—each station does its job well.

Design Principles:

• Modularization: Each step single clear function
• Context passing: Next step auto-references previous result
• Error handling: Each step validates output quality

Complete Workflow Example:

Workflow 1: Technical Blog Creation (5 steps)

Step 1: Topic Research
<task>
Research topic "React Hooks Best Practices":
- Analyze target reader pain points (1-2 years experience developers)
- List 5 core points
- Recommend 3-5 authoritative reference articles
</task>

Step 2: Outline Design
<task>
Based on research results, design article outline:
- Introduction (pain point resonance)
- 3-4 main sections
- Each section includes: concept, example, best practices, common mistakes
- Conclusion (summary + action recommendations)
</task>

Step 3: Content Creation
<task>
Create complete draft based on outline:
- Word count: 2000-2500 words
- Include runnable code examples
- Tone: Professional + conversational
- Integrate personal experiences and real cases
</task>

Step 4: Code Example Optimization
<task>
Check and optimize all code examples:
- Ensure runnable
- Add detailed comments
- Provide CodeSandbox links
- Include edge case handling
</task>

Step 5: SEO and Readability Optimization
<task>
Final optimization:
- Remove AI flavor (avoid "therefore," "in summary")
- SEO optimization (naturally incorporate keywords "React Hooks," "best practices")
- Readability improvement (short paragraphs, subheadings, lists)
- Add meta description and tag suggestions
</task>

Workflow 2: Code Review to Deployment (4 steps)

Step 1: Code Review
Review this PR, output:
- Code quality score (1-10)
- Main issues list
- Security risks (if any)
- Optimization suggestions

Step 2: Generate Test Cases
Based on code review results, generate:
- Unit tests (cover core logic)
- Integration tests (cover key flows)
- Edge case tests

Step 3: Update Documentation
Auto-update:
- API docs (if interface changed)
- README (if usage changed)
- CHANGELOG (record this update)

Step 4: Generate Deployment Checklist
Output pre-deployment checklist:
- Test pass status
- Database migration scripts
- Config file updates
- Rollback plan

Best Practices:

1. Specify Output Format Clearly Each step specifies output format for next step to use:

Step 1 output format:
[Core Points]
1. ...
2. ...

[Target Reader Pain Points]
- ...
- ...

Step 2: Design outline based on previous [Core Points]...

2. Validation and Error Handling

Step 3: Create draft
...

Validation:
- Is word count within 2000-2500 range?
- Includes at least 3 code examples?
- Integrated personal experiences?

If not met, please regenerate.

3. Claude Skills Application If using Claude Code, can create Skills (reusable Prompt templates):

• blog-research.md - Topic research step
• blog-outline.md - Outline design step
• blog-draft.md - Content creation step

Then use /blog-research to quickly invoke.

My Practical Experience: I now have 5 commonly used workflows, all templated. Each time just change topic and specific requirements, other steps execute automatically. Efficiency boost not just a little.

Part Four: Practical Scenario Integration

Tip 10: 5 Ready-to-Use Complete Workflows

These 5 workflows I actually use—genuinely practical.

Workflow 1: Technical Writing (Save 60% Time)

1. Topic research (Projects knowledge base + WebSearch)
2. Outline planning (System prompt: technical doc expert)
3. Content creation (Few-shot Examples + XML tags)
4. Code example generation (Artifacts create interactive demo)
5. SEO optimization (Extended Thinking deep analyze keywords)
6. Publishing (MCP auto-push to Notion + GitHub)

Workflow 2: Code Development (Save 50% Time)

1. Requirements analysis (System prompt: product analyst)
2. Architecture design (Extended Thinking + Projects tech docs)
3. Code implementation (Generate step-by-step, validate each)
4. Unit testing (Auto-generate based on code)
5. Doc generation (Artifacts create API docs)

Workflow 3: Data Analysis (Save 70% Time)

1. Data cleaning (Python code generation)
2. Exploratory analysis (System prompt: data analyst)
3. Visualization (Artifacts create interactive dashboard)
4. Insight report (Extended Thinking deep analysis)
5. Decision recommendations (Specific action recommendations based on data)

Workflow 4: Market Research (Save 65% Time)

1. Info collection (WebSearch + material organization)
2. Competitive analysis (System prompt: strategy analyst)
3. SWOT analysis (Extended Thinking)
4. Strategic recommendations (Projects + company background knowledge)
5. PPT generation (Artifacts create presentation)

Workflow 5: Content Creation (Save 55% Time)

1. Topic research (WebSearch + trend analysis)
2. Outline planning (System prompt: content strategist)
3. Draft creation (Few-shot Examples style imitation)
4. AI flavor removal (System prompt: humanizing editor)
5. SEO optimization (Natural keyword integration + meta info generation)

Usage Recommendations:

• Try one workflow first, get familiar then try others
• Adjust steps based on your needs
• Save commonly used workflows as templates

I use these workflows daily—they really elevated work efficiency to another level.

Conclusion

Alright, all 10 techniques shared. From basic system prompts, XML tags, to advanced Artifacts, Projects, MCP integration, to final Prompt chaining techniques.

Quick Recap:

1. Claude vs ChatGPT - Choose right tool
2. System prompts - Give Claude a “persona”
3. XML tags - Structure complex tasks
4. Few-shot Examples - Teach Claude to “learn by example”
5. Extended Thinking - Let Claude think deeply
6. Artifacts - Create interactive tools in 3 minutes
7. Projects - Say goodbye to repeating background
8. MCP integration - Connect workflows
9. Prompt chaining - Combo doubles efficiency
10. Complete workflows - 5 ready-to-use solutions

My Recommendation: Don’t try learning all techniques at once. Pick 1-2 most suitable for your current needs and try today. For example:

• If you often handle same project → Try Projects
• If you need to create tools or charts → Try Artifacts
• If your tasks are complex → Try XML tags + Extended Thinking

Claude is still rapidly evolving, with new features every few months. Keep following official updates, keep learning—you’ll discover AI tool potential far exceeds imagination.

Honestly, six months ago I really didn’t think Claude could be this powerful. Now it’s become my “second brain”—helps me write code, analyze data, create content, manage projects. 3x efficiency improvement isn’t exaggeration—it’s real experience.

What about you? Discovered any useful Claude techniques? Welcome to share in comments—might help more people.

AI tool’s true value lies in how you use it. Hope this article helps you fully unleash Claude’s complete potential.

Published on: Nov 24, 2025 · Modified on: Dec 4, 2025

Easton

AI & Intelligence