Sample chapters

Chapter 1 · Extract · 6 min read

The LLM-Augmented Marketer

The chapter that sets the worldview the rest of the Playbook runs on. It frames the shift from tool you sometimes use to augmentation you ship through, names the four jobs that change first and the three that change last, and builds the measurement spine every later chapter refers back to.

Chapter 1 is also the chapter most reviewers send to colleagues who are still on the fence. If the Playbook is going to land for your team, it lands here first. The extract below is the opening — Section 1 plus the first part of Section 2.

71% of organisations now use generative AI in at least one business function, with marketing and sales leading every other category (McKinsey, State of AI 2024). If you’re still treating LLMs as a faster copywriter, you’ve already missed the shift — the job itself is changing, not just the speed at which you do it.

The adoption curve is behind you. Generative AI moved through marketing faster than any technology before it. The category went from zero to 71% organisational adoption in roughly thirty months. The share of teams using it routinely climbed from 65% to 71% in a single six-month window.

Marketing and sales are the leading application area across industries, not the laggards. Whatever the arguments for experimentation looked like in 2023, the current question is not whether to adopt but how to structure the adoption so it compounds.

You almost certainly use an LLM already. You have favourite prompts, a few workflows where the model saves real time, and a rough sense of where it doesn’t earn its keep. That is surface-level fluency — the “I use it for subject lines and first drafts” plateau. It is where most marketing teams remain.

The plateau is stable. It’s also a ceiling. Teams that break through it rebuild something less visible than the stack: your own operating model — specifically, what work you keep in your head, what you push to a generator, and how you decide between the two.

Three specific shifts make the upgrade visible. You generate more drafts and ship fewer of them, because generation gets cheap and selection becomes the binding constraint. You spend less time writing and more time evaluating. Your taste is now your leverage — the part of the job that does not compress as the tools improve.

You stop counting pieces produced and start counting evaluations made and decisions closed. Once the model writes most of the pieces, the count stops discriminating between a strong week and a weak one.

Those three shifts are not incidental. They separate the teams capturing the top end of Adobe’s productivity distribution from the teams capturing half of that figure or less (Adobe, 2024 Marketer Productivity Study). Adobe’s average is 98 hours a year per marketer; the top end clears it well. They also underwrote the September 2025 finding that 93% of CMOs and 83% of marketing teams now report measurable ROI from generative AI (SAS / Coleman Parkes, September 2025).

Eighteen months earlier, Salesforce’s 9th State of Marketing showed only 32% of marketing organisations had fully implemented AI, with 43% still experimenting. The intervening period was the time it took the median team to rebuild its measurement layer around evaluation-era metrics. This chapter is about what to rebuild and how to start.

Where the hours move in the shift: producer-era marketers spent roughly 60% of active time drafting and 40% evaluating. LLM-augmented marketers invert the split — roughly 60% evaluating, 40% drafting and composing. The hours do not collapse; they move.

The Job to Be Done

Your job in this chapter is to orient yourself to what changed. The pre-LLM operating model — strategist, writer, designer, analyst, each owning their cell of the workflow — still works, but it is no longer the frontier.

The frontier is the LLM-augmented marketer. You compose work across roles by delegating the high-volume, low-judgement portions to a model and keeping the strategic, evaluative, and brand-voice portions.

Orientation gets harder, not easier, the longer you have been using ChatGPT or Claude casually. Casual fluency plateaus early. The plateau hides the deeper shift the opening framing named: your job is moving from producer to editor, from writer to taste-maker.

Worth Knowing. The producer-to-editor framing is not a metaphor and it is not a management-consulting reframe. It is a literal shift in where your hours go. Pre-LLM, a senior marketer spent roughly 60% of active time producing — drafting, composing, editing to spec — and roughly 40% judging.

An augmented marketer running the patterns in this chapter inverts that split within one quarter. The new 60% is evaluation, prioritisation, and brief-writing. Your hourly output of finished pieces does not change by 60%; your hourly output of decisions does.

The shift sounds abstract in the JTBD frame. The pain points in the next section make it concrete: five specific failure modes of the producer-era model. Each one compounds if you keep the old operating assumptions while reaching for the new tool.

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Chapter 3 · Extract · 9 min read

Content Marketing & Long-Form Writing

The chapter most readers come for. Five prompt templates spanning briefs, drafts, edit passes, distribution copy, and post-mortem reviews — each with a worked example, declared variables, and stated failure modes.

Chapter 3 is where the prompt-template format earns its keep. The extract below covers the opening three sections — the case for moving the LLM upstream and downstream of the draft — followed by the first and last templates from the chapter library: Pattern 1 (Voice-Calibrated Blog Brief) and Pattern 5 (Cross-Chapter Repurposing Brief).

84% of marketers say AI improved the quality of their content, and 82% of AI-using marketers created more content with AI assistance (HubSpot, State of Marketing 2024). But the teams compounding on those figures are not using LLMs at the drafting step. They use them at the brief and at the editorial critique — where the bottleneck of long-form content actually lives.

Content teams reach for LLMs to speed up drafting because drafting is where the hours feel most visible. They’re wrong about where the hours live.

For long-form content, the hours live upstream of the draft — in the brief quality that determines whether the draft can be anything other than generic. They also live downstream of it, in the editorial review that catches voice drift, thin claims, and SEO-and-reader mismatches before publication. Drafting is where the work happens; brief and critique are where the compounding happens.

The cost difference between the two seating decisions is large and almost always undercounted. Picture the per-piece economics of a 1,500-word B2B blog post at standard mid-market rates.

With LLM-at-drafting only, the cost is roughly:

• 30 minutes briefing (a topic, a length, a vague audience description)
• 10 minutes generating a draft
• 90–150 minutes of human revision because the brief did not give the model enough to work with

Total: 130–190 minutes per piece. A follow-on round of editorial rewriting lands whenever the angle is wrong.

With LLM-at-brief and LLM-at-critique, the cost is roughly:

• 25 minutes generating and selecting from three briefs
• 8 minutes generating the draft against the locked brief
• 15 minutes running the critique pass and reviewing the model’s revisions
• 30–40 minutes of human editorial

Total: 78–88 minutes per piece. The angle decides upstream and the surface-level fixes get caught before the human editor opens the file.

The reclaimed 50–100 minutes per piece is not idle time. It goes to the next chapter’s audience-angle work, the editorial calibration loop, or the briefs that stop the next round of generic drafts before they ship.

The Job to Be Done

Your job is to use LLMs where evaluative taste is the binding constraint in long-form content, and to leave drafting as the middle move in a longer composition. The binding constraints live upstream (a brief specific enough to produce non-generic output) and downstream (an editorial review that catches voice drift before publication). The drafting step, the one most marketers reach for the LLM to accelerate, is the least important place to put it.

The chapter’s argument: a composed workflow — brief → outline → draft → editorial critique — beats single-pass LLM drafting. With LLMs applied at the specification-heavy and evaluation-heavy ends, you get higher-quality content at faster cadence than a draft followed by heavy manual editing.

Worth Knowing. Drafting feels like the bottleneck because it’s the most visible step — hours spent staring at a blank page are memorable. But look at the invoice.

The per-piece cost of a generic brief is: draft, full edit, rewrite, re-edit, publish. The per-piece cost of a specified brief is: draft, light edit, publish. The brief is where the hours fight happens; they don’t fight visibly.

The Pain Points the Composed Workflow Resolves

Five pain points separate teams that compound on long-form content from teams that plateau. Each is resolved by applying LLMs at a different point than the drafting step.

Brief vacuum. A brief that reads “write a 1,500-word blog post on zero-party data, SEO keyword ‘zero-party data’” is not a brief — it’s a topic plus a length. The writer, human or model, has no audience, no angle, no desired take, no rejected takes, no voice samples, no body-copy reference. The output that follows is voice-neutral and commercially interchangeable. The first place to put an LLM is the brief-writing seat, where specification is the product.

Voice flattening at draft stage. LLMs default to voice-neutral prose because their training data is voice-neutral prose at scale — ask a model to draft in your voice without feeding it voice samples and the output is technically correct and commercially forgettable. The common fix, edit the draft toward your voice, does not scale past a handful of posts. The library fix — calibrate the draft with three voice samples from your best-performing posts — does. Show, don’t tell, applies to models too.

SEO-and-reader tension. Pre-LLM, you chose one or the other. Optimise for keywords and you produced thin ranked content; optimise for the reader and you produced content Google did not serve. The composed workflow resolves the tension by separating the two decisions: the SERP-aligned outline carries the structural SEO requirements, and the voice-calibrated draft carries the reader prose. Each gets the specification it needs.

Editorial review bottleneck. As content volume grows, manual editorial review becomes the new ceiling — the step that used to take thirty minutes per post now takes two hours across fifteen posts. An LLM critique pass previews issues before human review reaches its capacity ceiling: voice deviations, unsupported claims, thin sections. Human review then spends time on judgement calls, not on surface catches. The ceiling moves up.

Shallow repurposing. Teams repurpose a blog by chopping it into tweets. The tweets sound like they came from a blog because they did. Real repurposing extracts the insight first — the thing the reader will remember — then reshapes for each channel’s native format. The repurposing asset is the brief’s primary-angle field, not the full draft.

Worth Knowing. The five pain points share a cause: the LLM is in the wrong seat. Drafting is the seat most teams put it in; brief-writing and editorial critique are the seats where it earns its keep.

Every pain point in this section is a symptom of that seating decision. Move the LLM upstream and downstream and the symptoms fade together, not serially. Fix the seat assignment before you tune the prompts.

Two templates from the chapter library

The chapter ships five compositions. The first opens the workflow; the fifth bridges into Chapters 4, 5, and 6. Both are below — open either to read the prompt, the runnable version with worked-example values filled in, and the editorial commentary that follows in the chapter. Patterns 2, 3, and 4 — SERP-Aligned Outline, Long-Form Draft with Voice Samples, and Editorial Critique Pass — ship in the full chapter.

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