Prerequisites
This guide assumes you're familiar with basic prompt engineering concepts. If you're new to prompting, start with our beginner and intermediate guides first.
Beyond the Basics
Basic prompting gets you 80% of the way. Advanced techniques unlock the remaining 20%— which often makes the difference between good and exceptional results, especially for complex reasoning, multi-step tasks, and professional applications.
These techniques have emerged from AI research and real-world experimentation. They're not just theoretical—they're battle-tested methods that consistently improve output quality for challenging tasks.
40%+ accuracy improvement
on reasoning tasks with CoT
Complex workflows
handled via prompt chaining
Consistent outputs
through self-consistency
Chain-of-Thought Prompting
Chain-of-Thought (CoT) prompting encourages the AI to show its reasoning process step by step. This dramatically improves performance on math, logic, and multi-step reasoning problems.
Zero-Shot CoT
Simply add "Let's think step by step" to your prompt.
"A bat and ball cost $1.10. The bat costs $1 more than the ball. How much does the ball cost? Let's think step by step."Few-Shot CoT
Provide examples that demonstrate the reasoning process.
"Q: Roger has 5 tennis balls. He buys 2 cans of 3. How many now?
A: Roger starts with 5. 2 cans × 3 = 6. 5 + 6 = 11. Answer: 11"Analyze this business decision using chain-of-thought reasoning:
"Should our startup expand to the European market next quarter?"
Think through this step by step:
1. First, identify the key factors to consider
2. Analyze each factor with available data
3. Consider potential risks and mitigations
4. Weigh the pros and cons
5. Provide a reasoned recommendation
Show your complete reasoning process.Few-Shot Learning
Few-shot learning provides examples in your prompt to teach the AI the pattern you want. It's incredibly powerful for custom formats, specific styles, or domain-specific tasks.
Classify the sentiment of customer feedback.
Example 1:
Feedback: "The product arrived quickly and works great!"
Sentiment: Positive
Reason: Praises delivery speed and product quality
Example 2:
Feedback: "Terrible experience, never ordering again."
Sentiment: Negative
Reason: Strong negative language, indicates lost customer
Example 3:
Feedback: "It's okay, does what it says but nothing special."
Sentiment: Neutral
Reason: Balanced assessment, neither enthusiastic nor critical
Now classify:
Feedback: "Love the design but the battery dies too fast."
Sentiment:Pro Tip: Use 3-5 diverse examples that cover edge cases. Quality of examples matters more than quantity.
Prompt Chaining
Prompt chaining breaks complex tasks into a sequence of simpler prompts, where each step's output feeds into the next. This is essential for workflows that exceed what a single prompt can handle.
Example Chain: Content Creation Pipeline
Research & Outline
"Generate an outline for an article about [topic] with 5 main sections"
Expand Each Section
"Using this outline: [output 1], write detailed content for section 1"
Review & Refine
"Review this draft for clarity and engagement: [combined sections]"
Final Polish
"Optimize this article for SEO with keyword: [target keyword]"
Self-Consistency Technique
Self-consistency generates multiple responses and selects the most common answer. This reduces errors and increases reliability, especially for tasks with definitive correct answers.
How It Works:
- 1.Generate 3-5 responses to the same prompt (use temperature > 0)
- 2.Extract the final answer from each response
- 3.Select the most frequently occurring answer
- 4.Use majority voting for increased confidence
For each response, use the same prompt with temperature=0.7:
"Analyze this code and identify the bug:
[code snippet]
Explain your reasoning step by step, then state the bug clearly."
Run 5 times → If 4/5 identify the same bug → High confidence
Run 5 times → If answers vary widely → Problem may be ambiguousTree-of-Thought Reasoning
Tree-of-Thought (ToT) extends chain-of-thought by exploring multiple reasoning paths and evaluating which is most promising. It's powerful for problems requiring exploration and backtracking.
Problem: [Your complex problem here]
Explore this problem using tree-of-thought reasoning:
Step 1: Generate 3 different initial approaches
- Approach A: [describe]
- Approach B: [describe]
- Approach C: [describe]
Step 2: Evaluate each approach (rate 1-10 for feasibility)
Step 3: Expand the most promising approach with 2-3 sub-paths
Step 4: Evaluate sub-paths and select the best
Step 5: Continue until reaching a solution
Show your complete exploration tree.Crafting System Prompts
System prompts (or system messages) set the overall behavior, personality, and constraints for an AI assistant. They're crucial for building consistent AI applications.
You are [ROLE] with expertise in [DOMAIN].
## Core Behavior
- Always [key behavior 1]
- Never [constraint 1]
- When uncertain, [fallback behavior]
## Response Format
- Use [format preference]
- Include [required elements]
- Limit responses to [constraints]
## Tone and Style
- Maintain a [tone] voice
- Target audience: [audience description]
- Language level: [complexity level]
## Special Instructions
- [Domain-specific rules]
- [Edge case handling]
- [Error response format]Best Practice: Test your system prompt with adversarial inputs to ensure it handles edge cases gracefully.
Advanced Frameworks
These frameworks are designed for complex, professional use cases:
R.O.S.E.S Framework: Crafting Prompts for Strategic Decision-Making
Use the R.O.S.E.S framework—Role, Objective, Style, Example, Scenario—to develop prompts that generate comprehensive strategic analysis and decision support.
Learn moreT.R.A.C.E Framework: A Structured Approach for Technical Problem Solving with AI
Learn how to use the T.R.A.C.E framework—Task, Requirements, Audience, Context, Examples—to craft effective prompts for technical problem-solving, debugging, and development tasks.
Learn morePrompt Optimization
Systematic optimization improves prompt performance over time:
A/B Testing
Test prompt variations against each other with controlled metrics. Track accuracy, relevance, and user satisfaction.
Iterative Refinement
Start broad, identify failure modes, add specific instructions to address them. Document what works and why.
Token Efficiency
Remove redundant words, use efficient phrasing, structure for minimal tokens while maintaining clarity. Important for cost optimization at scale.
Temperature Tuning
Lower temperature (0-0.3) for factual/deterministic tasks. Higher (0.7-1.0) for creative tasks. Test to find optimal settings.
Professional Use Cases
Code Review Automation
Combine CoT + few-shot learning to review code for bugs, security issues, and best practices with consistent, thorough analysis.
Uses: CoT, Few-Shot, Self-ConsistencyResearch Synthesis
Chain prompts to gather information, identify themes, synthesize findings, and generate actionable insights from multiple sources.
Uses: Prompt Chaining, ToTCustomer Support Bots
System prompts + guardrails to create helpful, safe, on-brand AI assistants that handle edge cases gracefully.
Uses: System Prompts, Few-ShotData Analysis Pipelines
Multi-stage prompts for data cleaning, analysis, visualization suggestions, and insight extraction with verification steps.
Uses: Chaining, CoT, Self-Consistency