KERNEL Framework

# KERNEL Framework

A systematic approach to crafting effective AI prompts that deliver consistent, verifiable results.

## K - Keep it Simple

**Bad:** 500 words of context

**Good:** One clear goal

**Example:**
❌ Instead of: "I need help writing something about Redis"
✓ Use: "Write a technical tutorial on Redis caching"

**Result:** 70% less token usage, 3x faster responses

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## E - Easy to Verify

Your prompt needs clear success criteria.

Replace "make it engaging" with "include 3 code examples"

If you can't verify success, AI can't deliver it.

**Testing Results:** 85% success rate with clear criteria vs 41% without

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## R - Reproducible Results

Avoid temporal references ("current trends", "latest best practices")

Use specific versions and exact requirements

Same prompt should work next week, next month

**Consistency:** 94% across 30 days in testing

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## N - Narrow Scope

One prompt = one goal

Don't combine code + docs + tests in one request

Split complex tasks into focused requests

**Performance:** Single-goal prompts: 89% satisfaction vs 41% for multi-goal

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## E - Explicit Constraints

Tell AI what NOT to do

**Example:**
❌ "Python code"
✓ "Python code. No external libraries. No functions over 20 lines."

**Result:** Constraints reduce unwanted outputs by 91%

---

## L - Logical Structure

Format every prompt like:

1. **Context** (input)
2. **Task** (function)
3. **Constraints** (parameters)
4. **Format** (output)

**Complete Example:**

```
Context: Building a REST API for a task management app

Task: Write a Python function to validate user input for creating new tasks

Constraints:
- Use only Python standard library
- Max 20 lines per function
- Include error handling

Format: Return the function code with inline comments
```