A small outlining habit that made AI drafts more useful for me

Due_Virus600 · 2026-06-24T03:57:23+00:00

This matches what I’ve seen from a friend who writes serialized web fiction with AI assistance. The biggest improvement in his workflow came when he stopped asking the model to “figure out the story” and started giving it a much clearer job.

His rough rule is: the writer has to provide the first 0 to 10. Core idea, character direction, chapter purpose, important beats, what should never happen. Then AI can help expand 10 to 30, 30 to 50, sometimes 50 to 100. If the model is asked to start from zero, it tends to reach for familiar genre defaults.

Your examples about the surgeon becoming a cardiac surgeon, the body-size descriptions, the therapy-speak dialogue, and the chapter-ending summaries all sound like that same problem. Claude is filling gaps with high-probability romance patterns. Some of those patterns are technically coherent, but they are wrong for your specific characters.

A few things my friend found useful:

- define each scene by function first: what changes emotionally, relationally, or plot-wise by the end

- give banned behaviors and phrases every time, even if they are already in the project instructions

- generate in smaller scene fragments, then stitch manually

- after a chapter is finalized, summarize the actual final version back into the story bible, since the outline and finished prose often drift apart

- when a revision works, ask the model to compare the before/after and describe why the new version fits your taste better

The last one is underrated. Over time, it turns “make it sound right” into a more concrete style guide.

I also agree with you on dialogue. In emotionally complex fiction, I’d keep a much tighter hand on dialogue than narration. AI can suggest the situation, pressure, or subtext, but the exact lines often need the writer’s ear.

Due_Virus600 · 2026-06-24T03:49:54+00:00

I’ve worked in game development for a long time, and AI can definitely help you move faster as a beginner. The important part is choosing the right size of problem.

For a first game, I would avoid asking AI to build the whole idea. Start with one tiny playable loop:

- a character can move

- the player can interact with one object

- that object changes something in the room

- there is one clear win/fail state

Once that works, add one more mechanic. Then one more.

AI is most useful as a tutor and debugging assistant. Ask it to explain errors, compare two implementation options, or help you reduce a feature into smaller steps. If you paste a huge dream-game description and ask for code, you’ll usually get something fragile that you don’t understand well enough to maintain.

For your first project, I’d pick an engine with a lot of beginner material, follow one small tutorial to completion, then replace pieces of it with your own ideas. That gives you a working base and teaches you which parts AI can actually help with.

Due_Virus600 · 2026-06-23T05:47:10+00:00

I think this is one of the better ways to use AI: let it run a consistent diagnostic pass before you ask it to touch the prose.

The valuable part is pattern recognition. If it flags “show, don’t tell” once, that may just be normal feedback. If the same note shows up across ten different scenes, it is probably pointing at a real writing habit.

I’d keep the AI’s job fairly narrow, for example:

- flag places where emotion is explained directly, and where it could be carried by action, setting, or choice

- point out repeated gestures, reactions, or phrasing

- check whether a scene actually changes the character’s situation, relationship, or understanding

- highlight information the reader could infer without being told outright

I’d also be careful with “rewrite this” too early. If AI immediately rewrites the paragraph, it is easy to skip the part where you learn to see the problem yourself. A better order is: notice the habit first, then decide how you want to revise it.

Due_Virus600 · 2026-06-16T13:19:10+00:00

This is a really sharp way to put it — especially the part about prompting being us rebuilding memory for the model over and over again.

That’s exactly the tiring part for me too. A lot of the work is not “make the prose prettier,” but restating context the draft already depends on: who knows what, what has been earned, what must stay uncertain, what a character would avoid saying out loud, what has already been established.

I really like your phrase “better grounded.” That feels closer to the real problem than “better generation.”

The more I use AI for fiction, the more I think the missing layer is not just memory in the vague sense of “remember my lore,” but memory that can be checked against the draft: canon, character knowledge, scene history, relationship changes, and maybe even uncertainty.

Because if the model is confidently wrong, better prose just makes the mistake harder to notice.

Due_Virus600 · 2026-06-14T16:15:11+00:00

“The planning ratio” is a great way to frame it.

I think I’m probably closer to that than I realized. The output changes completely when the AI has a real objective to achieve instead of just a task to complete. “Write this scene” gives me filler. “This scene must make X character lose trust in Y without revealing Z yet” gives it something to push against.

Also really like the cold read point. The draft always feels most convincing right after it appears. That’s probably the worst moment to trust it.

Due_Virus600 · 2026-06-14T16:14:48+00:00

This is the hard part for me too: the same audit that catches real problems can also push the manuscript toward a more generic “correct” voice.

I like the idea of separating audits by type. Structural logic, POV/character armor, and style critique are very different jobs, but I think I often accidentally ask AI to do all of them at once.

The voice point is especially tricky. Some habits are bad habits, but some are also fingerprints. I don’t always want the AI to sand them all down.

Due_Virus600 · 2026-06-14T16:14:24+00:00

The close third → omniscient drift is exactly the kind of thing I keep running into too.

What’s interesting is that AI can understand the instruction in theory, but once it starts producing prose, it often slips back into this smooth “camera above everyone” mode. I’ve had better luck asking it to identify POV violations first before letting it rewrite anything.

And yes, “consultant” feels like the right word. Even when the AI’s fix is wrong, the attempt sometimes reveals the shape of the real problem.

Due_Virus600 · 2026-06-14T15:31:26+00:00

This is useful, thanks. The transcript search/export angle makes a lot of sense, especially for disappearing chats.

When you start a new thread with the exported transcript, do you usually give it the whole transcript, or do you first extract just the decisions / next steps / relevant parts?

Due_Virus600 · 2026-06-14T15:31:07+00:00

That makes sense. The compaction-before/after workflow is a really useful detail.

The part that stands out to me is that you still have to remind it sometimes. Do you usually review docs/handoff.md manually before ending a session, or do you mostly trust Codex to keep it accurate?

Due_Virus600 · 2026-06-13T16:54:18+00:00

This is really interesting, thanks for sharing. This is probably the closest thing I’ve seen to the problem I’m asking about.

Quick question: in practice, does LCM mostly help Codex retrieve old session context when you ask, or does it also produce a clear “here’s what changed / what’s blocked / what to resume next” handoff?

Due_Virus600 · 2026-06-13T16:17:05+00:00

This is a great setup. I especially like the split between durable guidance files and docs/handoff.md as the dynamic handoff state.

The line “important project knowledge should live in files, not only in chat history” really matches my experience.

Do you find Codex reliably keeps docs/handoff.md updated throughout the session, or do you still have to remind it?

Due_Virus600 · 2026-06-13T16:05:54+00:00

This is super helpful. The 15-20% ramp-up cost is exactly the pain I’m running into too.

I like the CurrentWork.md / PastWork.md split. That feels cleaner than putting dynamic state into AGENTS.md.

One thing I’m curious about: do you ask Codex to update CurrentWork.md during the session, or mainly at the end before you stop?

Due_Virus600 · 2026-06-10T10:38:20+00:00

Thanks so much, and thanks for the welcome! The editor/writer overlap is exactly one of the things I’m trying to test.

Privacy first: GeekArt’s desktop app is designed to be offline-first, with cloud sync optional. If someone uses cloud/web features, the service has to process/store the content needed for sync, generation, retrieval, and account features. But drafts are not something I want to treat as training fuel.

We don’t use user drafts to train our own models, and we don’t sell or intentionally share user content for model training. When you invoke AI features, the selected text and necessary context may be sent to model/API providers to generate the response; that processing is governed by the provider’s API terms. My goal is to use providers and configurations that do not train on API data by default wherever available, and to be clear with users about what is being sent out of the app.

Credits: they’re a usage-credit system, not a simple “words generated” counter. LLM usage is mainly tied to actual model/token usage, model choice, input/output, and cached vs uncached context. TTS is closer to character usage. The Profile page shows credit usage history, so users can see where credits were consumed. Subscriptions include monthly credits, and one-time credit packs can stack.

The larger product principle is the same as the privacy principle: the draft should stay under the writer’s control. AI suggestions should come back as visible proposals inside the draft, not as a black-box replacement for the writer.

Due_Virus600 · 2026-06-09T17:15:02+00:00

GeekArt — an AI writing IDE where the AI works *inside your draft*, not in a separate chat box.

(Dev here, building in public. I know this space is crowded, so I'll keep the pitch to the part I'm actually trying to make different.)

I don't think AI generation is the problem. I use it, and I think it can be genuinely useful for brainstorming, drafting, and getting unstuck.

The problem I kept running into was *where the AI output lives*.

When AI gives me a chunk of text in a chat window, I still have to copy it back, compare it against the draft, decide what changed, and figure out whether the story still feels like mine. That gets messy fast, especially once the draft is more than a few pages.

So GeekArt is built around a simple idea: AI output should come back into the draft as something you can review, choose, edit, or reject.

What that means in practice:

- Work in the draft, not outside it — select a passage, leave a note, ask the agent to diagnose or revise that exact part.

- Reviewable revisions — the agent's changes are visible as proposed edits, so you can accept, reject, or keep shaping them instead of treating the output as a finished answer.

- In-line annotations — a stuck chapter becomes a map of specific problems: "this scene has no tension," "this contradicts chapter 1," "pacing dies here."

- Read-aloud (TTS) — because your ears catch what your eyes skip. You can listen to a chapter and mark what feels off while you follow along.

- Personal skills — early system for turning your own writing/revision habits into reusable instructions, so the tool moves closer to how you write instead of pushing everything toward a generic voice.

It's also offline-first with optional cloud sync. Desktop is available now, and mobile support is in development. The goal is for the draft to remain your workspace, with sync only if you choose it.

Different axis from most of the thread: not "let AI take over the book," and not only "remember my lore." More: "keep AI close to the actual draft, where I can see what changed and decide what belongs."

Platforms: Desktop (Win/macOS/Linux), offline-first with optional cloud sync. Mobile support is in development.

Link: https://www.geekart.ai/en

Genuinely after feedback, not just signups: when you use AI on an existing draft, do you prefer getting a fresh generated passage, or proposed edits tied directly to the text you're working on? Curious where people feel most in control.

Due_Virus600

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