Advanced Prompting Techniques: Chain-of-Thought, Tree-of-Thought, Few-Shot and More

Beyond Basic Prompting

If you already master prompting basics, it is time to move to advanced techniques that achieve significantly better results from AI models. These techniques, derived from artificial intelligence research, exploit the deep capabilities of large language models to solve complex problems.

This guide covers the main advanced techniques with clear explanations, practical examples, and application advice.

Zero-Shot Prompting

Principle

Zero-shot prompting asks the model to perform a task without providing prior examples. It is the simplest form of prompting, but it can be optimized.

When to Use It

• For simple, well-defined tasks
• When the model was likely trained on similar tasks
• For rapid prototyping before refining with other techniques

Example: Classify this customer review as positive, neutral, or negative: "The product arrived late but the quality is excellent"

Optimizing Zero-Shot

Even in zero-shot, you can improve results by being precise about output format, defining a role for the AI, and specifying decision criteria.

Few-Shot Prompting

Principle

Few-shot prompting provides the model with a few examples of the desired task before submitting the actual question. These examples demonstrate the expected pattern.

How to Structure Examples

• Optimal number: 2 to 5 examples usually suffice
• Diversity: cover different scenarios
• Consistency: keep identical format for all examples
• Representativeness: choose examples close to your real case

Example for support ticket classification:

Classify each ticket into a category. Examples:
Ticket: "I cannot log in since this morning" -> Category: Technical issue
Ticket: "How do I change my password?" -> Category: Usage question
Ticket: "Your service is terrible, I want a refund" -> Category: Complaint
Ticket: "Can you add a PDF export feature?" -> Category: Feature request

Now classify this ticket: "The application crashes when I open a file larger than 10 MB"

Chain-of-Thought (CoT)

Principle

Chain-of-Thought encourages the model to detail its reasoning step by step before giving its final answer. This technique significantly improves performance on logical, mathematical, and analytical reasoning problems.

Implementation

The simplest method is to add "Think step by step" or "Explain your reasoning" to your prompt.

Example: A store offers 20% off all items, then an additional 10% discount for members. If an item costs 150 euros, how much does a member pay? Think step by step.

Zero-Shot vs Few-Shot CoT

Zero-shot CoT simply uses the reasoning instruction. Few-shot CoT provides examples of complete reasoning, which is more effective for complex tasks.

Tree-of-Thought (ToT)

Principle

Tree-of-Thought extends Chain-of-Thought by exploring multiple reasoning paths in parallel, like a decision tree. The model evaluates each branch before choosing the best one.

Practical Implementation

Example: To solve this problem, explore 3 different approaches. For each approach: 1) Describe the method, 2) Apply it step by step, 3) Rate its reliability out of 10. Then choose the most reliable approach and give the final answer.

Use Cases

• Problems with multiple possible solutions
• Complex strategic decisions
• Comparative scenario analysis
• Creative problem solving

Self-Consistency

Principle

Self-consistency generates multiple responses to the same prompt then selects the most frequent answer (majority vote). This technique reduces random model errors.

Implementation

Ask the model to solve the same problem in 3 to 5 different ways, then compare results and retain the majority answer.

Prompt Chaining

Principle

Prompt chaining breaks down a complex task into sequential sub-tasks, where one prompt's output becomes the next one's input.

Chain Example

• Prompt 1: Analyze a text and extract main themes
• Prompt 2: For each theme, generate arguments for and against
• Prompt 3: Synthesize into a structured recommendation

Advantages

Chaining enables better quality control at each step, finer context management, and the ability to intervene between steps.

Role Prompting (Persona)

Principle

Assigning a specific role to the model to orient its responses according to particular expertise.

Example: You are a cybersecurity expert with 15 years of experience in the banking sector. Analyze this network architecture and identify potential vulnerabilities: [description]

Best Practices

• Define the role's expertise and experience
• Specify the professional context
• Indicate the expected level of detail
• Combine with other techniques (CoT + Role)

Retrieval-Augmented Generation (RAG)

Principle

RAG enriches the prompt with information retrieved from an external knowledge base. This technique allows the model to respond with up-to-date information specific to your context.

Application in Prompts

Even without full RAG infrastructure, you can apply the principle by including documentation excerpts, recent data, or domain-specific examples directly in your prompt.

Combining Techniques

Advanced techniques are often more effective when combined:

• Role + CoT: an expert who reasons step by step
• Few-shot + CoT: examples with detailed reasoning
• ToT + Self-consistency: multiple exploration with verification
• Chaining + Role: different experts at each step

Conclusion

Advanced prompting techniques transform how we interact with AI models. By mastering and judiciously combining them, you can achieve remarkable quality results on complex tasks. The key is to experiment and find the combinations that work best for your specific use cases.