HumanAugmentation Featured

Prompting Is Dead. Long Live the Loop.

Prompting alone won’t cut it. By reframing AI work as a structured loop of context, feedback, and iteration true collaboration emerges not from clever one-liners but from designing fluid, adaptive interactions where meaning is shaped together.

Travis Frisinger

• 2 min read
Prompting Is Dead. Long Live the Loop.
A loop between human and AI, symbolizing ongoing dialogue and co-creation where interaction replaces instruction.

Most people think prompt engineering is the key to getting value from LLMs.

It’s not.

The real unlock is how you frame the interaction—and that means understanding the AI decision loop and the role of coherence.

The week I fit into a day

Just yesterday this is what I achieved.

I wrapped my LambdaConf talk deck.
Synthesized 12 stakeholder interviews into a governance readout.
Finished a DevSecAgentOps playbook.
And still carved out time to push my side project: a permission-aware, enterprise-grade RAG platform.

That’s not hustle culture. That’s the new normal—if you’re working with AI the right way.

Why most people plateau with LLMs

They chase better prompts.
They copy recipes.
But prompt engineering is tactical—it’s brittle. You end up tweaking surface-level inputs and wondering why nothing clicks.

The real unlock isn’t in the prompt.
It’s in the frame—how you engage the model, the structure of the interaction, the loop you run with it.

The AI Decision Loop (Revised for Coherence)

This is how I operate now. Not by prompting, but by navigating:

  1. Frame the Intent
    I don’t start with “generate X.”
    I start with “here’s the situation, here’s what matters, here’s where I’m at.”
    It’s like onboarding a new team member, not issuing a command.
  2. Watch for Coherence
    I’m not just chasing accuracy—I’m tracking structure.
    Does it hold the thread? Build logically? Match the energy? That’s coherence.
    It’s what makes the output usable and extensible.
  3. Navigate the Loop
    I treat the conversation like a feedback loop.
    I prune. I explore. I build forward.
    Every pass clarifies the shape of the solution.
  4. Own the Judgment
    The model isn’t deciding—it’s offering structure to react to.
    My taste and direction still drive the work.
    I stay in the loop, and that keeps the quality high.

See the diagram below for a visual summary.

Circular flowchart showing Context → Prompt → AI Response → Human Feedback → Refined Prompt
Figure 1: The AI collaboration loop—start with clear context, craft a prompt, review the AI’s response, provide human feedback, then refine the prompt to drive continuous improvement.

AI as a thinking partner

This isn’t about replacing people.
It’s about accelerating them.

The model doesn’t generate brilliance.
You do—by creating the right interaction space for it to build within.

Why It Works

It’s not about having a magic prompt.
It’s about building a feedback system where the model becomes your collaborator.
It’s because I don’t treat the LLM like a tool—I treat it like a thinking partner inside a loop:

  1. Frame the Goal – I give it context as if I’m onboarding a teammate
  2. Test Coherence – I look for signal, not perfection. Does it hold structure while remaining accurate?
  3. Navigate the Loop – I refine by working with the model, not over it
  4. Own the Judgment – I’m the human in the loop. The model assists, but doesn’t decide

Takeaway

Prompting is dead.
Long live the loop.

The real value of LLMs emerges when you shift from commanding to collaborating.
From tweaking prompts to mastering structure.
From rigid engineering to fluid work.

We’re not at the end of software.
We’re at the beginning of something faster, more dynamic, and way more human.