KERNEL is model-agnostic
KERNEL is model-agnostic
Distillation — one idea, in my words
- Write the irreducible insight. 50–120 words.
- State the claim/definition in one sentence first.
- Remove source phrasing; keep mechanism or rule.
- If you feel the need for “and,” split into another atom.
The benefits of KERNEL arise from prompt specification, not model quirks. Clear goals, constraints, and structure improve outcomes across GPT, Claude, Gemini, Llama.
Why it matters – 1-3 bullets on utility, mechanism, implication.
- What decision does this change?
- What prediction does this enable?
- What failure does this prevent?
- Portable playbook across vendors.
- Lowers switching costs and lock-in risk.
- Enables shared internal standards.
Links
- Does this collide/agree with an existing atom?
- Add at least one forward link to a molecule/canonical note.
- Add one tag-like topic (2–5 terms, not a dump).
- Broader topic: #platformstrategy
- Related atoms: KERNEL — Reproducible results
- Upstream source note:
Source excerpt (optional)
Paste exact quote or figure caption.
“Works consistently across models.”
Citation block (optional)
Source: {{source}} • Page: {{page}} • Key: {{citekey}}
volodith. “After 1000 Hours of Prompt Engineering, I Found the 6 Patterns That Actually Matter.” Reddit, r/PromptEngineering, September 29, 2025. https://www.reddit.com/r/PromptEngineering/comments/1nt7x7v/after_1000_hours_of_prompt_engineering_i_found/.