The Coherence Trap: Why LLMs Feel Smart (But Aren't Thinking)
LLMs don’t think—they resonate. In this article, I unpack The Coherence Trap' the illusion of intelligence created by structured, fluent language. We explore how meaning emerges from alignment, not understanding, and why designing for coherence—not cognition—is the key to building with AI.
When GPT-4 launched, something strange happened. Prompts that once yielded stiff or robotic replies now returned answers that felt... uncanny. The responses weren’t just correct—they were coherent. They carried tone, structure, even momentum. It felt like you were talking to something that understood you.
But it wasn’t understanding. It was coherence.
The Trap
Large Language Models (LLMs) like GPT-4o don’t reason. They don’t plan. They have no self-awareness. Yet they feel intelligent.
This is the Coherence Trap—the illusion of cognition created by language that resonates with structure and context. We assume meaning because it fits. But what’s really happening under the hood is something else entirely.
How Coherence Emerges
LLMs generate tokens by predicting the most likely next word based on context. But this prediction is shaped by a massive set of latent structures—syntax, tone, prior associations, narrative forms, and more. When these layers align, they create what I call coherence reconstruction.
Coherence reconstruction is not understanding. It’s the echo of form, reinforced across attention layers. It’s what makes a model appear consistent, insightful, or even witty—without actually thinking.
The more your prompt scaffolds the right structure, the more likely the model is to “lock in” to that coherence and hold it across outputs. This is why prompt design often feels like magic.
Why Hallucinations Happen
Hallucinations aren’t bugs. They’re symptoms of structured resonance operating without anchors.
When the model lacks grounding—say, via retrieval or tightly framed prompts—it still seeks to complete the pattern. It will generate plausible, fluent nonsense that fits the shape, even if it breaks truth. The structure is working, even if the facts aren’t.
From Compression to Reconstruction
Much of AI theory still frames these systems as compression engines: store, compress, retrieve.
But LLMs offer something different: structured resonance. They rebuild meaning in the moment, using distributed activation patterns. The power isn’t in what they remember—it’s in how they reassemble coherence in context.
Design for Structured Resonance
This insight has profound implications. If LLMs aren’t thinking, but instead resonating, then we must shift how we design systems around them.
- Prompting = Interface Design: Treat prompts as scaffolds, not one-offs.
- Structure = Stability: Use consistent tone, layout, and loops to reinforce resonance.
- Drift = Debug Signal: Coherence breaking down? It’s a sign your structure lost alignment.
- RAG = Anchoring Gravity: Dense, relevant context pulls resonance toward truth.
Engineering Implications
Designing for structured resonance changes the engineering stack itself:
- Agent Design: Agents should be structured as orchestration layers that maintain coherence across tasks, not linear logic chains. Think modular, conversational state management over deep planning trees.
- PromptOps: Prompt engineering becomes a systems discipline. You'll need versioned, reusable prompt templates with enforced structure, scoped memory, and embedded feedback loops.
- Evaluation: Traditional eval metrics like BLEU or ROUGE fall short. Instead, we must measure coherence under pressure—can the model hold its tone, task, and flow across turns and failures?
- Debugging: Engineers should learn to recognize coherence drift the way they track memory leaks. It's a systemic fault, not a one-off glitch.
- Architecture: Multi-layer systems (e.g. Retrieval-Augmented Generation) must be designed to reinforce coherence, not just supply facts. Anchoring mechanisms, alignment feedback, and context shaping become first-class citizens.
- User Interfaces: UX needs to reflect iteration loops and contextual reinforcement—not static chat boxes. Interfaces should help users co-steer resonance, not just send prompts into the void.
This isn’t just a new feature layer. It’s a reframe of what AI engineering means. We’re not building brains—we’re building coherence engines.
Final Thought
You’re not talking to a thinker. You’re standing in front of a mirror with 8,001 dimensions. The model doesn’t understand you—but it reflects back a shape that feels like it does.
That’s the trap.
Stop chasing intelligence. Start designing for structured resonance.