The KERNEL Framework
Six principles that transform prompts from creative experiments into reliable engineering assets.
The Six Principles
Each letter represents a core principle for enterprise-grade prompts
Why It Matters
Multiple objectives create ambiguity. The AI must guess priorities, leading to inconsistent results.
Analyze this code for bugs, suggest performance improvements, refactor it to use modern ES6 syntax, and write unit tests.
Analyze this code and identify any bugs or logical errors. For each bug found, explain the issue and its potential impact.
Why It Matters
If you can't verify the output is correct, you can't trust it in production.
Make this API documentation more engaging and user-friendly.
Rewrite this API documentation to include: 1) A one-sentence summary, 2) Three code examples (cURL, JavaScript, Python), 3) A table of all error codes.
Why It Matters
Production systems need predictability. A prompt that works today but fails tomorrow is unreliable.
Summarize the latest trends in cybersecurity.
Based on the OWASP Top 10 vulnerabilities published in 2023, summarize the three most critical threats to web applications.
Why It Matters
Narrow prompts are easier to test, debug, and maintain. They reduce token usage and improve accuracy.
Design a complete CI/CD pipeline including build, test, security, deployment, monitoring, and rollback for GitHub Actions, GitLab CI, and Jenkins.
Prompt 1: Design the build and test stages for a Node.js microservice CI/CD pipeline. Prompt 2: Design the security scanning stage. Prompt 3: Design the deployment strategy for blue-green deployment.
Why It Matters
Constraints prevent the AI from making assumptions or taking unwanted shortcuts. They ensure safe, compliant output.
Write a Python script to scrape product data from a website.
Write a Python script to scrape product data. Constraints: - Use only requests and BeautifulSoup - Include 2-second delay between requests - Do not bypass authentication - Handle HTTP errors with try/except
Why It Matters
Structure improves AI comprehension by 31%. It prevents the model from conflating context, instructions, and data.
I have a CSV file with sales data and I need to analyze it to find the top 5 products by revenue but only for Q4 2024...
### Context I have a CSV file with sales data. ### Data Structure Columns: product_id, product_name, sale_date, quantity, price ### Task Find top 5 products by revenue for Q4 2024. ### Output Format DataFrame with: product_name, total_revenue
The KERNEL Template
### Context [Provide relevant background information] ### Task [State the single, clear objective] ### Constraints - [What the AI should NOT do] - [Required libraries, formats, or standards] - [Any limitations or boundaries] ### Input [Describe the input data or information] ### Output Format [Specify exactly what the output should look like]
The Impact
First-try success rate (vs 72% for ad-hoc prompts)
Reduction in token usage
Improvement in AI comprehension with logical structure
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Browse our library of 300+ prompts built with the KERNEL framework