Advanced Prompt Engineering Techniques for Claude and ChatGPT in 2026

Prompt engineering has evolved from simple instructions to sophisticated techniques that dramatically improve AI output quality. This guide covers advanced strategies that work across Claude and ChatGPT, plus model-specific optimizations.

Why Prompt Engineering Still Matters

Models are smarter in 2026, but they’re not mind readers. The difference between a vague prompt and a well-structured one is the difference between mediocre output and production-ready results.

Good prompts reduce hallucinations, improve accuracy, and save time. Bad prompts waste tokens and produce work you’ll need to redo.

Core Techniques

1. Chain-of-Thought (CoT) Reasoning

Chain-of-thought prompting asks the model to show its work. Instead of jumping to an answer, it reasons through the problem step by step.

Basic prompt:

What is 15% of 240?

CoT prompt:

What is 15% of 240? Show your reasoning step by step.

The CoT version produces:

1. Convert 15% to decimal: 0.15
2. Multiply: 240 × 0.15 = 36
3. Answer: 36

This matters for complex problems where the reasoning path affects accuracy. Math, logic, and multi-step analysis all benefit from CoT.

When to use CoT:

• Mathematical calculations
• Logical reasoning
• Multi-step analysis
• Debugging code
• Complex decision-making

When to skip CoT:

• Simple factual queries
• Creative writing
• Brainstorming
• Tasks where process doesn’t matter

2. Few-Shot Learning

Few-shot learning provides examples of the desired output format. The model learns the pattern and applies it to new inputs.

Zero-shot (no examples):

Extract the company name and funding amount from this text:
"Anthropic raised $450M in Series C funding."

Few-shot (with examples):

Extract the company name and funding amount from these texts:

Text: "OpenAI secured $10B from Microsoft."
Company: OpenAI
Amount: $10B

Text: "Google invested $300M in Anthropic."
Company: Anthropic
Amount: $300M

Text: "Anthropic raised $450M in Series C funding."
Company: ?
Amount: ?

The few-shot version is more reliable. The model understands exactly what format you want.

Best practices:

• Use 2-5 examples (more isn’t always better)
• Make examples diverse but consistent in format
• Include edge cases if relevant
• Order examples from simple to complex

3. Scaffolding

Scaffolding breaks complex tasks into smaller steps. Instead of asking for everything at once, you guide the model through a structured process.

Without scaffolding:

Write a product requirements document for a mobile app.

With scaffolding:

Let's create a product requirements document for a mobile app. We'll do this in steps:

Step 1: Define the core problem this app solves.
Step 2: Identify the target users.
Step 3: List the key features.
Step 4: Define success metrics.

Start with Step 1.

Scaffolding improves quality by forcing the model to think through each component before moving forward.

4. Output Anchoring

Output anchoring specifies the exact format you want. This is especially useful for structured data.

Vague:

Analyze this customer review.

Anchored:

Analyze this customer review and provide:
- Sentiment: [Positive/Negative/Neutral]
- Key themes: [List 2-3 main points]
- Action items: [What should the company do?]
- Confidence: [High/Medium/Low]

The anchored version produces consistent, parseable output.

5. Role Assignment

Assigning a role gives the model context about expertise level and perspective.

Generic:

Explain quantum computing.

With role:

You are a computer science professor explaining quantum computing to undergraduate students who understand classical computing but have no physics background. Use analogies to classical concepts.

The role-based version produces more appropriate explanations.

Model-Specific Strategies

Claude-Specific Techniques

1. XML Tags for Structure

Claude responds well to XML-style tags for organizing complex prompts:

<task>
Analyze this code for security vulnerabilities.
</task>

<code>
[Your code here]
</code>

<focus_areas>
- SQL injection
- XSS vulnerabilities
- Authentication issues
</focus_areas>

<output_format>
For each vulnerability found:
- Location: [file:line]
- Severity: [Critical/High/Medium/Low]
- Description: [What's wrong]
- Fix: [How to fix it]
</output_format>

2. Thinking Tags

Claude can use <thinking> tags to reason privately before responding:

Before answering, use <thinking> tags to:
1. Identify the core question
2. Consider edge cases
3. Plan your response structure

Then provide your answer.

3. Extended Context Utilization

Claude Opus 4.6 has a 1M token context window. Use it:

I'm providing three research papers below. After reading all three, synthesize the key findings and identify areas of agreement and disagreement.

<paper1>
[Full text]
</paper1>

<paper2>
[Full text]
</paper2>

<paper3>
[Full text]
</paper3>

ChatGPT-Specific Techniques

1. System Message Optimization

ChatGPT’s system message sets persistent behavior:

System: You are a senior software engineer reviewing code. Be direct and specific. Focus on correctness, security, and maintainability. Don't praise obvious things.

User: Review this function...

2. Temperature Control

ChatGPT lets you adjust temperature. Use it strategically:

• Temperature 0.0-0.3: Factual tasks, code generation, data extraction
• Temperature 0.4-0.7: General writing, explanations
• Temperature 0.8-1.0: Creative writing, brainstorming

3. Function Calling

ChatGPT’s function calling feature structures outputs:

{
  "name": "extract_entities",
  "description": "Extract named entities from text",
  "parameters": {
    "type": "object",
    "properties": {
      "people": {"type": "array", "items": {"type": "string"}},
      "organizations": {"type": "array", "items": {"type": "string"}},
      "locations": {"type": "array", "items": {"type": "string"}}
    }
  }
}

Advanced Patterns

Multi-Turn Memory

For complex tasks spanning multiple messages, explicitly maintain context:

We're building a REST API. I'll provide requirements in stages. After each stage, summarize what we've decided so far before moving to the next stage.

Stage 1: Authentication
[Discussion]

Summary so far:
- JWT-based auth
- 15-minute access tokens
- 7-day refresh tokens

Stage 2: Rate limiting
[Continue...]

Constraint Specification

Be explicit about constraints:

Write a product description with these constraints:
- Length: 150-200 words
- Tone: Professional but approachable
- Must include: "enterprise-grade", "scalable", "secure"
- Avoid: Superlatives, buzzwords, vague claims
- Target audience: CTOs and engineering directors

Error Correction Loops

Build self-correction into prompts:

Generate a SQL query for this requirement: [requirement]

After generating the query:
1. Check for SQL injection vulnerabilities
2. Verify all table/column names exist
3. Confirm the query returns the expected data shape
4. If you find issues, revise the query

Provide both the final query and a brief explanation of any corrections made.

Common Mistakes

1. Vague Instructions

Bad:

Make this better.

Good:

Improve this paragraph by:
- Reducing word count by 30%
- Replacing passive voice with active voice
- Adding specific examples
- Maintaining the core message

2. Assuming Context

Bad:

Fix the bug.

Good:

This function should return the sum of even numbers in the array, but it's returning the sum of all numbers. Fix the bug.

[Code]

3. Overloading Single Prompts

Bad:

Analyze this data, create visualizations, write a report, and suggest next steps.

Good:

Step 1: Analyze this data and identify key trends.
[Wait for response]

Step 2: Based on those trends, what visualizations would be most effective?
[Continue...]

4. Ignoring Output Format

Bad:

List the pros and cons.

Good:

List the pros and cons in this format:

Pros:
- [Pro 1]
- [Pro 2]

Cons:
- [Con 1]
- [Con 2]

Overall assessment: [1-2 sentences]

Practical Templates

Code Review Template

Review this [language] code for:
1. Correctness: Does it do what it's supposed to?
2. Security: Any vulnerabilities?
3. Performance: Any obvious inefficiencies?
4. Maintainability: Is it readable and well-structured?

For each issue found, provide:
- Location: [file:line]
- Severity: [Critical/High/Medium/Low]
- Issue: [What's wrong]
- Fix: [Specific code change]

Code:
[Your code]

Data Analysis Template

Analyze this dataset:

<data>
[Your data]
</data>

Provide:
1. Summary statistics (mean, median, std dev for numeric columns)
2. Data quality issues (missing values, outliers, inconsistencies)
3. Key patterns or trends
4. Recommended next steps for deeper analysis

Format your response with clear sections and use tables where appropriate.

Writing Improvement Template

Improve this text for [target audience]:

Original:
[Your text]

Improvements needed:
- Clarity: [Specific issues]
- Conciseness: [Target word count]
- Tone: [Desired tone]
- Structure: [Desired structure]

Provide:
1. Revised version
2. Brief explanation of major changes
3. Word count comparison

Testing and Iteration

Good prompt engineering is iterative. Test your prompts:

1. Start simple: Begin with a basic prompt
2. Identify failures: Where does it produce wrong or inconsistent results?
3. Add constraints: Address specific failure modes
4. Test edge cases: Try unusual inputs
5. Refine format: Adjust output structure
6. Document patterns: Save successful prompts for reuse

Measuring Prompt Quality

Track these metrics:

• Accuracy: How often does it produce correct results?
• Consistency: Does it produce similar outputs for similar inputs?
• Completeness: Does it address all requirements?
• Efficiency: Token usage vs. output quality
• Robustness: How well does it handle edge cases?

The Bottom Line

Advanced prompt engineering is about clarity, structure, and iteration. The techniques in this guide—chain-of-thought reasoning, few-shot learning, scaffolding, output anchoring, and model-specific optimizations—dramatically improve AI output quality.

Start with clear instructions, provide examples, structure complex tasks, and specify output formats. Test your prompts, identify failure modes, and refine iteratively.

The difference between basic and advanced prompt engineering is the difference between using AI as a toy and using it as a production tool. Master these techniques, and you’ll get consistently better results from both Claude and ChatGPT.