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Semantic Vector Markup Language - Learn to Speak in LLM
AI Attention Precision
Ultra-rare control tags act as gravity wells—drift drops 40%
Prompt Engineering is Dead.
Long Live Cognitive Engineering!
Most AI interaction is built on false assumptions.
Prompt Engineering spam is one of the Root Causes.
Dismantling the Myths
"Expert" Prompts Activate Specialized Knowledge
Telling AI to "act as an expert" doesn't access deeper knowledge—it just shifts word probabilities toward arbitrary formality.
You're triggering stylistic mimicry, not knowledge retrieval. This theatrical direction wastes precious tokens on vocabulary performance when you could be using them for substance.
Attention Mechanism Reality
AI Reads Prompts Linearly Like Humans
AI loads the entire context into semantic vector space simultaneously—no sequential "reading" occurs.
When you structure prompts for human reading, you're optimizing for the wrong thing entirely. This leads to burying critical information where it gets minimal attention.
More Detailed Instructions Produce Better Results
Each word creates connections to every other word—100 words = 10,000 competing relationships.
Exhaustive prompts create attention traffic jams. Over-specification doesn't make AI more careful—it makes it more confused, forcing it to process hundreds of thousands of relationships instead of focusing on your core intent.
Relationship Complexity
No quadratic explosion
AI Processing Reality
AI Constructs Thoughts Sentence by Sentence
Text generation is linear only because humans require sequential output—internal processing happens in high-dimensional vector space.
When you structure prompts as if explaining to someone who needs step-by-step logic, you're solving a problem that doesn't exist. This leads to redundant scaffolding that wastes tokens without adding clarity.
Specific Word Choice is Crucial for Understanding
Words are merely coordinate labels in vast semantic vector space—geometric relationships matter infinitely more than labels.
When you obsess over finding the "perfect" word, you're optimizing the wrong variable. This vocabulary fixation distracts from what matters—relationship structure—leading to linguistically elegant but structurally muddy prompts.
Semantic Equivalence
Relationship pattern preserved despite vocabulary change
Translation Layers
Intent → Vector relationships
Natural Language is the Only Interface
Natural language is a massively lossy translation layer—AI's native processing operates on vector relationships, not prose.
When you force all communication through natural language prose, you're like a programmer writing code through telephone. This translation layer introduces ambiguity and wastes tokens on grammatical structures that don't affect AI processing.
The Interlocking Problem
These myths don't exist in isolation—they reinforce each other in a vicious cycle that multiplies failures and wastes effort.
The Cascade
Expert role-play + verbose instructions = maximum token waste with minimum precision
The Trap
Linear thinking + word obsession = beautiful prose that completely misses the point
The Result
All myths together = working ten times harder for one-tenth the results
The Fundamental Realization
We're not "talking" to AI—we're programming attention patterns in high-dimensional vector space using the blunt instrument of natural language. Once you see this clearly, the need for a more precise notation system becomes obvious.