Prompt Engineering (Gemini)
The RICE acronym is a framework for writing effective prompts for large language models. It's a structured approach that ensures you provide the AI with all necessary components to generate a high-quality and relevant response.
While the exact meaning of each letter can vary slightly depending on the source, the most common and effective breakdown is:
R - Role: Assign a specific persona or role to the AI. This helps the model adopt a particular tone, style, and perspective, which is crucial for getting the desired output.
Example: "You are a senior Python developer," "Act as a professional copywriter," or "You are a travel agent specializing in eco-tourism."
I - Instructions: Clearly and explicitly state the task you want the AI to perform. This is the core of your prompt. Break down complex tasks into smaller, sequential steps if necessary.
(Myron) Some versions of RICE say I is input or information.
Example: "Write a blog post about the benefits of remote work," "Generate a Python script to scrape a website," or "Summarize the key findings from this research paper."
C - Context / Constraints: Provide all the relevant background information and details. This helps the AI understand the purpose of the task and the specific environment in which the response will be used. This is also where you add any limitations or rules.
Example: "The blog post should be for a young, freelance audience," "The script must handle dynamic content and avoid getting blocked," or "The summary should be no more than 200 words and written in a formal tone."
E - Examples: Include one or more examples of the desired output. This is one of the most powerful components of prompt engineering.
By showing the AI what a good response looks like, you guide it toward the style, format, and content you want.
Example: "Here is an example of a script we've used before: [provide code snippet]," "Here's a paragraph that matches our brand's voice: [provide text snippet]."
Why RICE is So Effective
By including all four elements, you eliminate ambiguity and provide the AI with a comprehensive set of guidelines. A prompt that only has "I - Instructions" (e.g., "Write a blog post") is likely to produce a generic response.
However, a prompt that uses the full RICE framework leaves very little to chance, resulting in a more accurate, useful, and consistent output. It transforms a simple request into a precise command.
EXAMPLES OF PROMPTS (Gemini)
General Development
How can I optimize a Python script for processing large datasets in-memory, considering both CPU and memory usage?
Provide a comprehensive guide on setting up a CI/CD pipeline for a Python-based web application using GitHub Actions and Heroku.
Design an asynchronous task queue with Python and Redis for a high-traffic e-commerce platform, detailing the producer-consumer model and error handling.
What are the key differences between asyncio and multiprocessing in Python, and in what scenarios is each preferred for concurrent programming?
How do you implement robust logging in a microservices architecture using Python, ensuring traceability and centralized log management?
Data Science and Machine Learning
Given a large CSV file, write a Python script that uses pandas and dask to perform efficient feature engineering and preprocessing on a machine with limited RAM.
Create a scalable data pipeline for real-time sensor data using Python, Kafka, and a NoSQL database, including schema validation and data transformation.
How can I deploy a PyTorch model to a production environment using FastAPI, optimizing for low latency and high throughput?
Develop a Jupyter Notebook that demonstrates advanced data visualization techniques for exploring a time-series dataset, including anomaly detection and seasonality analysis.
Write a script to perform hyperparameter tuning for a scikit-learn model using optuna or hyperopt, including a detailed explanation of the optimization algorithm.
Security and DevOps
Implement secure authentication and authorization for a Flask REST API using JWTs, including token refreshing and blacklisting.
Create a script to automate the provisioning of a cloud infrastructure on AWS using Boto3 and CloudFormation templates.
How can I perform a security audit on a Python codebase, identifying common vulnerabilities such as SQL injection, XSS, and insecure deserialization?