Where we are
You've picked a task, built your context library, and planned everything: persona, tools, process sequence, model strategy. All of that lives in your context.md. Now it's time to hit go. Planning is over. You're executing.
This part covers the full execution cycle: generating a first draft, giving it eyes through screenshot verification, and iterating with structured feedback until the output is ready to send. The first draft won't be perfect, and that's by design. What matters is how quickly you can move from rough to polished.
The three-pass iteration strategy
The first draft won't be perfect, and that's by design. Trying to get to 100% in one pass wastes tokens and usually fails. The fastest path to "done" is rough, refined, polished. Each pass has diminishing scope but increasing precision.
The approach is: create a first draft, then maybe a second draft with more general bulk prompts, and then do surgical edits as needed. Each pass narrows the scope. You go from "fix everything" to "fix this one heading."
The first draft: let it run
The first prompt is simple: run the full process sequence. Claude reads the inputs, applies the brand system, and generates the complete output. The key here is to let it run without interrupting. You're looking for a complete first attempt, not a perfect one.
What V1 looked like
The first draft had the right colours and structure, but several things were off. The logo was broken, the content read like a dump of sporadic facts in white tiles rather than a coherent narrative, and Claude used the screenshot tool to create the PDF but never actually read those screenshots to verify the output.
Correct brand colours applied throughout. Page structure and layout followed the brief. All source files were read and referenced.
Logo rendering was broken. Content was sporadic, not narrative. No visual self-verification was performed.
Claude won't self-check its own visual output unless you tell it to. It will generate HTML, even take screenshots, but it won't look at those screenshots and compare them against the brief. This has to be explicit in your instructions.
Context window management
After V1 generation, the context window was at 54% usage. Before giving feedback, I used /compact to summarise and preserve tokens. This freed up space for the detailed feedback that would follow.
Give your AI eyes
The screenshot verification loop is how you give your AI workflow visual awareness. Without it, Claude generates HTML and renders PDFs blindly. The process is straightforward but must be explicit in your instructions:
After discovering this gap in V1, the screenshot verification step was encoded into context.md as a permanent requirement. Two verification passes minimum. This is the kind of instruction that compounds: you discover it once and it improves every future run.
Structured feedback and V2
Before giving feedback, I wrote down on a piece of paper what are some of the general things I noticed. Then I dictated the list as structured feedback. This approach works better than ad-hoc corrections because Claude can address everything in one pass rather than going back and forth.
The feedback list
V2 results
V2 took about seven minutes and came back at roughly 90-95%: a photo on the cover pulled from the inspiration folder, better narrative flow, and charts built with Plotly. The structured feedback approach meant Claude could address everything in one pass rather than needing multiple rounds of individual corrections.
Writing feedback down first, then delivering it as a structured list, is more effective than giving corrections one at a time. You save tokens and Claude can make better decisions about how changes interact with each other.
Feedback encoding
This is the step that creates compounding returns. Every piece of feedback you give goes back into context.md permanently. The corrections stop being one-off fixes and become rules that apply to every future run.
"Fix the footer text." "Two verification passes minimum." "Use vector outline icons only."
A brand rule, a process step, and a design principle, all permanent, all automatic.
This is what separates AI workflows from one-off prompting. In a regular chat, you correct the same mistakes over and over. With feedback encoding, every correction makes every future run better. "Fix the footer" becomes a brand rule. "Two verification passes" becomes a process step. "Use vector outline icons only" becomes a design principle.
Your context.md is a living document. It gets better with every project because every piece of feedback becomes a permanent rule. This is how 60 minutes of setup turns into 7 minutes per document.
What's next
You've now taken a first draft from roughly 60% to something you'd actually send, using three passes with decreasing scope and increasing precision. Every correction was encoded back into context.md. The workflow works. But does it work again, on a completely different topic, with zero additional setup?
In Part 5, you'll prove it. You'll run a completely different topic through the same context.md and see whether the investment was worth it. Then we'll recap the four building blocks and what you've accomplished.