--- slug: refining-ai-output-edit-panel title: "Refining AI Output with the Edit Panel Without Burning Credits" excerpt: "How to edit AI generated spec sections without regenerating from scratch. The prompt patterns that work and the ones that waste your credit budget." primaryKeyword: "edit AI generated spec" publishedAt: 2026-06-09 readingTimeMin: 7 author: "Robert Boylan" tags: - draftlytic - ai-edit - prd - refinement - app-planning --- You hit Generate. Draftlytic builds the spec. You read through it and most of it is exactly right, but the target audience section is too broad, one feature is missing an acceptance criterion, and the tech stack has a framework you've already ruled out. Your two obvious options are: live with it, or start over and lose all the good stuff. Most people pick one of those two. There's a third option that most users skip over entirely: edit the AI-generated spec section by section, without touching the parts that are already right. The AI Edit Panel (the pencil icon on any section in your project) is how you do that. It lets you rewrite one section at a time with a short prompt. It's 10 credits per edit on standard depth, 15 on detailed. It's not chat. It's surgical. And it produces much better results than regenerating the whole thing, if you use it right. This post covers when to use it, why scoping matters more than you think, and the five prompt patterns that consistently get good output. ## When to edit vs when to regenerate Not every problem needs a fix, and not every fix needs a regeneration. Here's a rough decision tree: **Live with it** if the section is close enough that the AI coding tool reading the spec will get the intent. AI tools like Lovable, Cursor, and Bolt.new aren't reading your PRD like a lawyer reads a contract. If "target audience: developers" is in the right ballpark, it'll produce something useful. Only edit when the gap will actually change the output. **Use the Edit Panel** when you have a specific, contained change. The tech stack should be Supabase, not Firebase. Feature 4 needs acceptance criteria. The copy tone is formal and should be casual. These are scoped. They touch one section. The Edit Panel handles them in one turn. **Regenerate from scratch** when the first pass was so far off that fixing each section individually would take longer than starting over. That's rare. Usually the first generation gets most things right and only a few sections need work. The Edit Panel is designed for the middle case, which is also the most common case. A spec that's 80% right and needs three targeted fixes is not a reason to start over. ## Why scoping matters more than your prompt wording The single biggest mistake with the Edit Panel is treating it like a chat interface with the whole project. When you write "improve the features," the AI has to guess which features, what "improve" means, and in what direction. It gets defensive and makes small, generic changes. You spend credits, nothing meaningful shifts, and you feel like AI editing doesn't work. When you write "add acceptance criteria to feature 3, the CSV export feature," the AI knows exactly what it's touching, what's missing, and what success looks like. It rewrites that one feature description cleanly and leaves everything else alone. The difference isn't the quality of your writing. It's the scope. **Tighter scope produces better output, every time.** This is the same reason that, when you look at [what a Draftlytic PRD export actually contains](/blog/inside-an-exported-prd), each section is structured separately. The AI thinks in sections. Give it a section boundary and it works within that boundary. Take the boundary away and it sprawls. Practical scope rule: if your prompt contains the word "and," split it into two edits. "Update the target audience and expand the tech stack" should be two separate panel opens, two 10-credit edits. Each one will be cleaner than trying to do both at once. ## Additive prompts vs replacement prompts There are two fundamentally different things you can ask the Edit Panel to do. **Additive prompts** add something that isn't there yet: - "Add acceptance criteria covering what happens when the upload fails." - "Add a constraint: the app must work offline." - "Add two more must-have features: user profile editing and notification preferences." Additive prompts are lower-risk. If the AI overshoots, the new content is clearly new and easy to spot or remove. **Replacement prompts** change something that already exists: - "Change the tech stack from Firebase to Supabase." - "Rewrite the target audience to focus on solo founders, not enterprise teams." - "Rename feature 2 from 'Social Login' to 'Passwordless Auth' and update the description to match." Replacement prompts need to be more explicit, because the AI has to decide what to keep and what to swap out. Vague replacement prompts produce partial replacements. "Make the copy tone more casual" will shift the tone of the first few sentences and leave the rest formal. "Rewrite the entire copy tone section in a casual, direct voice, no corporate phrasing" does what you mean. When in doubt, write the edit like you're leaving instructions for someone who hasn't read the spec. Don't assume the AI will infer what you meant from context. ## Five edit patterns that consistently work These patterns cover most of the editing work you'll actually do. **1. Rename and reframe.** When the AI generated a feature or section heading that doesn't quite match your mental model, use a rename edit first, then a separate expansion edit. "Rename feature 5 from 'Settings' to 'Account Management' and update the description to reflect that this covers billing, profile, and notification preferences." One prompt. Clean result. **2. Add acceptance criteria.** Features without acceptance criteria are the most common reason AI coding tools produce things that are almost right. The spec said "CSV export" but didn't say what columns, what file name format, whether it exports one page or all pages. "Add acceptance criteria to the CSV export feature: it should export all rows visible in the current filtered view, use the format `export-YYYY-MM-DD.csv`, and show a success toast when done." **3. Trim and focus.** Generated text tends to run long. If a section has three sentences where one would do, a trim edit is worth running. "Shorten the overview section to two sentences. Keep the core value proposition and the target audience. Drop the implementation detail about the database." **4. Contradiction fix.** Sometimes two sections in the generated spec contradict each other. The target audience says "non-technical founders" but the tech stack assumes React expertise. "The tech stack section currently lists React. Update it to suggest a no-code or low-code alternative that matches the non-technical founder audience from the target audience section." **5. Reorder by priority.** Feature lists that come out of generation are often in the order the AI thought of them, not the order they should be built. This one's a straight swap. "Move the authentication feature to the top of the must-have list. It needs to come before the dashboard, not after it." ## When to use the PRD Workshop instead The Edit Panel and the [PRD Workshop](/blog/prd-workshop) are both about refining output, but they work on different problems. The Edit Panel is scoped and immediate. One section. One prompt. One result. Good for targeted fixes where you know exactly what's wrong. The PRD Workshop is conversational and wide-ranging. You paste the whole spec and have a back-and-forth with an AI tuned to the specific coding tool you're about to use (Cursor, Lovable, v0, Bolt.new, and others). It's better when you want to reshape the spec around a different goal, when you're handing it to a different tool than you originally planned, or when you want an outside read on what's weak before you paste it anywhere. A typical workflow that works well: use Draftlytic to generate the initial spec, run two or three Edit Panel fixes on the obviously wrong sections, then take the result into the PRD Workshop when you want a tool-specific polish before the first coding session. Use the Edit Panel when you know what to fix. Use the Workshop when you're not sure what's wrong. ## The takeaway Most specs are fixable. The problem is that the default response to "this isn't quite right" is to either accept it or start over, when a series of focused, scoped edits would close the gap in under five minutes. The Edit Panel is built for that middle path. Keep your prompts narrow, split anything that contains "and," and be explicit about what should stay the same as much as what should change. The AI follows precise instructions well. It follows vague ones politely but unhelpfully. If you're paying for Draftlytic and not using the Edit Panel, you're leaving a lot of output quality on the table.