Contrastive Prompting: Definition and Examples
A prompt engineering technique that involves providing the model with examples of what it should do AND what it should not do, in order to refine its understanding of the task through contrast.
Full definition
Contrastive Prompting is an advanced prompt engineering method based on a simple but powerful principle: show the AI model not only the desired outcome, but also the outcome to avoid. By presenting contrasting pairs—a good example and a bad example—the model can precisely grasp the nuances of what is expected.
This technique is inspired by contrastive learning approaches used in machine learning, where a model learns to distinguish similar items from dissimilar ones. Applied to prompting, it leverages the ability of large language models to deduce patterns from positive and negative demonstrations provided in the context.
Concretely, instead of just saying 'Write a concise summary,' Contrastive Prompting involves showing an example of a well-written summary alongside a summary that is too long or off-topic, explaining why one is correct and the other is not. This juxtaposition allows the model to calibrate its output with significantly higher precision than simple instructions alone.
Contrastive Prompting is particularly useful when quality criteria are subjective or difficult to articulate in words. Rather than multiplying complex instructions, the model infers implicit rules from the direct comparison between what is desired and what is not.
Etymology
The term combines 'contrastive' (from Latin contrastare, 'to stand in opposition'), borrowed from the field of contrastive learning in machine learning, and 'prompting', denoting the art of formulating instructions for an AI model. The concept emerged around 2023 in the NLP research community, directly inspired by work on contrastive learning such as SimCLR and CLIP.
Concrete examples
Writing marketing content with a specific tone
Write a welcome email for our SaaS.
✅ GOOD example (desired tone):
"Welcome aboard! Your account is ready. Let's explore together what you can accomplish starting today."
❌ BAD example (tone to avoid):
"Dear user, we inform you that your account has been successfully created. Please find attached the documentation."
Write in the style of the good example: warm, action-oriented, without corporate jargon.
Sentiment classification with ambiguous cases
Classify the sentiment of these customer reviews.
✅ Positive: "The product does the job, I'm pleasantly surprised" → Positive (satisfaction expressed despite low expectations)
❌ Not positive: "The product does the job, nothing more" → Neutral (lack of enthusiasm, implicit reservation)
Now classify: "The product does the job, it's okay"
Code generation following specific conventions
Write a Python function for email validation.
✅ Expected style:
def validate_email(email: str) -> bool:
"""Validate email format."""
pattern = re.compile(r'^[\w.+-]+@[\w-]+.[\w.]+$')
return bool(pattern.match(email))
❌ Style to avoid:
def checkEmail(e):
if '@' in e and '.' in e: return True
else: return False
Follow the good style: typing, docstring, robust regex, snake_case naming.
Practical usage
To apply Contrastive Prompting, first identify the typical errors the model makes on your task, then build 'good vs. bad' pairs that precisely illustrate the boundary between acceptable and unacceptable results. This technique is particularly effective for tone control, classifying ambiguous cases, and adhering to stylistic conventions. Start with 2-3 contrasting pairs and adjust based on the results obtained.
Related concepts
FAQ
What is the difference between Contrastive Prompting and Few-Shot Prompting?
How many contrasting pairs should be included in a prompt?
Does Contrastive Prompting work with all AI models?
See also
How to use this prompt
- Copy the prompt with the button above.
- Paste it into ChatGPT, Claude or your favorite AI assistant.
- Replace the bracketed variables with your details, then refine the result.
About Prompt Guide
Prompt Guide is a free library of 2500+ ready-to-use prompts for ChatGPT, Claude and other AIs, with guides to learn prompting and tools to build and optimize your own prompts.
More definitions
CrewAI: Definition and Examples
CrewAI is an open-source Python framework for orchestrating multiple collaborative AI agents, each with a specific role, goals, and tools.
Cross Attention: Definition and Examples
Attention mechanism that allows a model to relate two different sequences, such as an image and a text, so that each element of one sequence can attend to elements of the other.
Cursor: Definition and Overview of the AI Editor
Understand Cursor: AI-native code editor based on VS Code. Differences with Claude Code, GitHub Copilot, and Windsurf, concrete use cases.
Custom GPT: Definition and How to Create Your Own
Understand OpenAI's Custom GPTs: pre-configured ChatGPT assistants. Step-by-step creation, differences with Claude Skills and Gemini Gems.
Data Augmentation: Definition and Examples
Data augmentation is a technique that consists of artificially enriching a training dataset by creating variations of existing data
Datasheets For Datasets: Definition and Examples
Methodology proposing systematic documentation of datasets used in artificial intelligence, akin to technical datasheets accompanying electronic components.
Get new prompts every week
Join our newsletter.