The Kaggle paper, “Prompt Engineering,” provides a comprehensive guide on how to improve interaction with language models (LLMs) through effective query design. It discusses the basic principles of question engineering, phrasing patterns, strategies such as chain-of-thought, the importance of role and context, and tools for assessing response quality. It also provides practical applications in programming and data analysis, making it useful for Kaggle users.
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| Prompt Engineering Full Power of AI with Smarter Questions |
Question engineering is the skill of designing queries in a directed manner to encourage a language model (such as ChatGPT or GPT-4) to provide accurate and effective responses. Key principles include:
- Clarity of wording: Avoid ambiguity or general statements.
- Context: Include reference information to improve model comprehension.
- Role adoption: Such as saying “act like a doctor” to customize style and content.
- Chain-of-thought reasoning techniques: Encourage the model to reason step by step.
- Evaluate and improve results through iteration.
OpenAI stated that the old user customizations remained unchanged after this update.
This change, while seemingly minor, could radically transform how humans and machines interact.
The update relies heavily on improved real-time engineering methods that modify AI responses based on user instructions.
This approach isn’t entirely new. Users could already modify bot responses during live conversations by saying something like, “Answer me in a fun and entertaining way.”
But now, the design is easier to use.
Even with these improvements to customization, several hurdles remain. Some have raised concerns about clarity and the ability for users to distinguish between “modified” AI responses and regular responses.
Using a playful tone in professional settings could impact the authenticity of the interaction. This could prompt the company to establish strict rules regulating permissible changes.
What is question engineering?
Question engineering is the process of strategically designing queries (prompts) to encourage large language models to produce accurate and appropriate responses. This process involves identifying appropriate phrasing, identifying context, and adjusting parameters such as temperature, top-k, and top-p to adjust the creativity and accuracy of responses.
Advanced Question Engineering Techniques
The paper includes advanced techniques such as:
- Chain-of-Thought: Encouraging the model to provide logical steps to solve complex problems.
- React: Combining reasoning with the ability to interact with external tools such as search engines or programming interfaces (APIs).
- Contextual Questioning: Providing specific background information to improve response accuracy.
- Role Questioning: Directing the model to adopt a specific role (such as a programmer or technical writer) to guide its response.
Best Practices in Question Engineering
Best practices include:
- Simple and Clear Design: Writing clear and straightforward queries to avoid ambiguity.
- Use examples: Provide one-shot or few-shot examples to illustrate the requirements.
- Documentation and iteration: Record different query attempts and analyze their results to improve performance.
Practical applications on Kaggle
In the context of Kaggle competitions, prompt engineering can be used to improve:
- Code generation: Writing accurate and efficient code.
- Code debugging: Detecting errors and proposing solutions.
- Code explanation: Providing clear explanations of the written code.
- Code translation: Converting code between different programming languages.
For full details, you can refer to the paper: Prompt Engineering on Kaggle.
