LLMs promise faster emotional resonance in copy, but speed without scrutiny slides into sameness. The solution lies in structured feedback loops that keep human judgment central while leveraging AI pattern recognition.
The Feedback Loop Imperative
Emotional engagement is the point of copy. If the reader does not feel seen, they move on. Large language models (LLMs) help us reach resonant language faster, but speed without feedback collapses into autopilot. A simple, durable feedback loop, draft, test with real readers, adjust, keeps the work honest.
McLuhan's tetrad provides a useful lens here: what LLMs enhance, what they make obsolete, what they retrieve, and how they can reverse into their opposite. Apply that lens to copywriting and you get a clearer map for using AI without losing human judgment, cognition, and craft.
A working definition: a feedback loop is an iterative pass where you use reader responses, comments, small test reads, qualitative notes, to refine emotional tone, clarity, and relevance. The approach sounds basic because it is. The discipline lies in doing it every time.
Enhancement: What LLMs Make Easier
LLMs enhance pattern access. They surface turns of phrase and tonal variations that match a reader's inner voice. They can generate multiple takes quickly so you can compare how each one lands. In practice, that looks like:
- Rapid exploration: 10 headlines that speak to the same core problem in different emotional registers, calm, urgent, wry, reassuring.
- Targeted revision: asking for “plainer, more grounded” language or a tighter hook while keeping the message intact.
- Tone alignment: rewriting for empathy without padding, or trimming sentimentality without losing warmth.
Used this way, the model is not the writer; the model serves as a pattern amplifier. You still apply judgment. You still decide which version carries real weight. The feedback loop starts as soon as you compare variants and notice what rings true versus what feels hollow. That noticing is part of structured thinking, and where your cognition does the real work.
Obsolescence and Retrieval: The Shifting Skillset
Over time, heavy reliance on generative outputs can dull certain muscles. Rigid outlines and stepwise logical proofs show up less in first drafts. That represents the obsolescence side of the tetrad: fewer passes spent building arguments from scratch.
But something useful is retrieved. LLMs can nudge us toward emotional intelligence, naming feelings, anticipating reader reactions, and balancing logic with care. Consider the spirit of approaches like Dialectical Behavior Therapy (DBT), which integrates emotion and reason. You can mirror that integration in your process:
- Empathic modeling: write a short note that answers, “What might this reader be feeling before they see this message? What do they hope changes after?” Keep it in plain language. Use it as a north star for tone.
- Micro-bridge reasoning: stitch one small inference at a time, problem, lived consequence, specific relief, so the reader can discover the point with you. No leaps. No grand claims.
- Friction checks: after a model draft, ask, “Where does this copy talk at the reader instead of with them?” Mark those spots and tighten.
This is retrieval at its best: a renewed focus on emotional clarity without losing coherence.
If logic fades, bring back light scaffolding, clear problem framing, evidence in simple terms, and a visible path from tension to relief. Not a rigid system, just enough structure to keep thinking straight.
Reversal: When Speed Erodes Depth
Push any tool to the extreme and it flips. With LLMs, velocity can produce sameness, rhythms, metaphors, and promises that read like everything else. That represents the reversal: a tool meant to enhance resonance can drain it.
Typical signals of reversal:
- Interchangeable phrasing that could sell anything to anyone.
- Emotional tone that spikes on sentiment and dips on specificity.
- Safe, generic claims that never risk a concrete detail or a clear stance.
Feedback loops are the brake. Put small, human tests between draft and publish. Ask a handful of readers to mark the line that felt most true and the line that felt most manufactured. If the “manufactured” list grows, slow down. Rewrite with fewer qualifiers. Trade abstractions for ordinary, verifiable moments.
A simple self-check: if you removed brand names and the copy still works for five unrelated products, you have crossed into formula. Pull it back to the lived problem and the narrow promise you can actually keep.
A Working Cadence for Authentic Copy
You do not need a heavy process to stay honest. You need a cadence you will repeat. Here is a pragmatic loop that fits most writing contexts:
1) Clarify the reader state
- Two sentences: what they face, what they want changed.
- Name the feeling in plain words (e.g., anxious, skeptical, hopeful). Keep this at the top of the doc.
2) Generate options with intention
- Use the LLM to produce variants with distinct tonal goals (e.g., steady, empathetic, no-frills). Limit to 3–5 to prevent choice fatigue.
- Ask for one “tighter and plainer” pass. Ask for one “more grounded, fewer adjectives” pass.
3) Human pass for structure
- Apply micro-bridge reasoning: Problem → Lived consequence → Specific relief → Next step.
- Cut hedges and filler. Keep one concrete detail per key point.
4) Small reader test
- Share with a few real readers. Ask three questions: What felt true? What felt manufactured? Where did you stop caring?
- Capture phrases they use in feedback; those are signals for the next revision.
5) Adjust tone, not just words
- If feedback flags sentimentality, reduce emotional language and increase specificity.
- If feedback flags coldness, add a short acknowledgement of the reader's context and a credible, bounded promise.
6) Document what resonated
- Track which lines or moves consistently land. This becomes your light “thinking scaffold” for future drafts, supporting structured cognition without rigid templates.
This loop protects emotional engagement without outsourcing judgment. The model stays in its lane: exploring options, suggesting patterns, and speeding up iteration. You stay responsible for coherence, voice, and truth.
Closing: Using the Tetrad to Stay Oriented
Through McLuhan's tetrad, LLMs in copywriting look less mystical and more manageable:
- Enhancement: fast pattern exploration and tonal shifts.
- Obsolescence: less reliance on strict, from-scratch logic.
- Retrieval: renewed attention to emotional intelligence and reader empathy.
- Reversal: a slide into formula if speed outruns discernment.
The fix is not heroic. The fix requires consistent feedback loops and clear thinking.
Use the model to open possibility, then use human cognition to choose, shape, and own the words. That tension, speed and scrutiny, emotion and structure, is where durable resonance lives.
To translate this into action, here's a prompt you can run with an AI assistant or in your own journal.
Try this…
After your next AI-generated copy draft, ask three readers: What felt true? What felt manufactured? Where did you stop caring? Use their exact phrases to guide your revision.
