Here is the summary of key improvements made:
1. Structure & Flow
- Decision Trees: Clear branching logic with ├── and └── notation
- Sequential Steps: Numbered, ordered procedures instead of scattered explanations
- Prerequisites: Explicit dependency checks before proceeding
2. AI Agent Optimizations - Tool Call Clarity: Exact function names and parameters
- Binary Decisions: Clear yes/no conditions instead of ambiguous language
- Error Handling: Specific failure conditions and next steps
- Verification Steps: "Recheck" instructions after each fix
3. Cognitive Load Reduction - Reference Tables: Quick lookup for tools and purposes
- Pattern Recognition: Common issue combinations and their solutions
- Critical Reminders: Common AI mistakes section to prevent errors
4. Actionable Language - Removed verbose explanations mixed with instructions
- Consolidated multiple documents' logic into single workflows
- Used imperative commands: "Check X", "If Y then Z"
- Added immediate verification steps
Wait, are we about to reinvent programming from first principles?
Great! A diviner has vibe-exposed the arcane magic word knowledge on the steps to ultimate knowledgeplasty! Come let us get together to share more trial-and-error wordsmithery, Together we will someday have ultimate power!
If the model creators themselves arent sharing this magic-word bullshitteryy then why is anyone spending time on this? It is just going to change with every model release
In other words, just like programming, we’re writing better instructions. In this case, we’re asking it to think out loud more clearly. It’s almost like whiteboard interview prep.
It’s quite amazing because it means programming is fully entering the natural language phase of the timeline.
If you aren’t a solid clear writer, you may not make it in the brave new world.
I’ve found myself writing code intending to write prompts for writing better code.
Soon enough Im sure we’ll start to see programming languages that are geared towards interacting with llms