What a Transformer Layer Does
Transformers are the architecture behind modern LLMs. They process information through stacked layers, each refining the representation.
Each layer:
- Applies attention (determines which tokens influence which)
- Mixes contextual signals across the sequence
- Rewrites token representations based on context
Meaning changes at every layer — until it stops.
The Attention Mechanism
Attention answers: "Which other tokens should this token pay attention to?"
Example sentence: "The bank by the river was steep."
When processing "bank," attention looks at surrounding tokens. "River" and "steep" signal the geological meaning, not financial.
This happens through learned weights:
- Query: What am I looking for?
- Key: What do I contain?
- Value: What information do I provide?
Attention scores determine how much each token influences the final representation.
Layer-by-Layer Refinement
Early layers (1-10):
- Capture surface patterns
- Build basic syntactic structure
- Identify entity types
Middle layers (10-40):
- Resolve ambiguity
- Lock in semantic interpretation
- Commit to meaning
Late layers (40+):
- Refine for output
- Prepare for generation
- Apply task-specific adjustments
Meaning Lock-In
By mid-layer depth, ambiguity collapses. The model commits to a single interpretation.
This is irreversible within a single forward pass.
If the model interprets "Apple" as a fruit in layer 15, it won't reconsider at layer 50. The interpretation is locked.
Late clarification rarely works.
Why Early Context Dominates
Position in the context window matters.
Content that appears early:
- Gets processed through all layers
- Establishes the interpretive frame
- Influences how later content is understood
Content that appears late:
- May not fully propagate before lock-in
- Must fit within established interpretation
- Cannot override earlier signals
Visibility Implication
Critical signals must appear early:
| Signal Type | Must Be Established Early |
|---|---|
| Entity type | "Gong is a revenue intelligence platform" |
| Audience | "For B2B sales teams" |
| Scope | "Enterprise-grade" or "SMB-focused" |
| Intent | "Analyzes calls to improve win rates" |
What fails:
- Footnotes with key information
- FAQs buried at page bottom
- Critical context in expandable sections
- Entity definitions only in the glossary
The Attention Budget
Transformers have finite attention capacity.
In long documents:
- Not all tokens attend to all other tokens equally
- Attention becomes sparse
- Early tokens often receive disproportionate attention
This is why document structure matters for AI visibility. Front-load your most important claims.
Practical Recommendations
For AI-visible content:
- State what you are in the first sentence
- Define your category immediately
- Lead with concrete claims, not context-setting
- Don't bury differentiators in later sections
What to avoid:
- Long introductions before substance
- "We'll explain X later" patterns
- Critical facts only in appendices
- Assuming the reader will scroll
Key Takeaway
Once meaning is locked, the rest is execution.
AI commits early. Your content either signals correctly in the first pass, or it doesn't signal at all.