u/masonga1960

I run a niche acting channel. It's small but growing (more slowly than I'd like). The filming and editing are the fun part for me. What kills me is everything after: making a thumbnail, writing the title and description, clipping shorts with captions, scheduling everything across multiple days. That stuff takes longer than the actual video.

I was bouncing between Canva for thumbnails, a transcription tool for captions, manually clipping shorts in my editor, copy-pasting descriptions with the same footer every time, then uploading each short one by one into YouTube Studio. For a month of content that was easily two full days of work.

I ended up hacking together my own workflow that handles most of it in one pass. Not polished, not pretty, but it cut those two days down to about four hours. The point isn't the tool though. The point is I didn't realize how much time I was losing until I mapped it all out.

So what's eating your time? Is it the same post-production grind, or is your bottleneck somewhere else entirely? Curious what the actual pain points are for people in different niches.

I've been using AI to analyze full manuscripts for the past couple months. Not to write them. To read them and figure out what's working and what isn't, across dozens of novels, hundreds of chapters, thousands of prompt variations.

And here's what surprised me. Spending that much time on the ANALYSIS side completely changed how I think about using AI on the CREATION side. When you watch how these models actually process prose at scale, you start seeing patterns that explain why your AI co-writing sessions sometimes produce gold and sometimes produce...not-gold.

Your AI doesn't know your book. Don't assume it does even if you told it at the start of the session.

When I ran analysis on manuscripts, the results were sometimes garbage until I told the model the POV mode, tense, and character names up front. Without that, it guessed. Usually badly.

The same thing applies when you're using the AI to draft prose. If you're asking an LLM to draft a scene, continue a chapter, or punch up dialogue, and you haven't given it the ground rules for YOUR story... it's filling in the blanks with its own defaults.

Third person past tense. Generic character names it half-remembers from earlier in the conversation. A tone that drifts toward whatever its training data says "fiction" sounds like.

Two sentences fix this. "This is a first-person present-tense noir. The narrator is Frank, a PI in Cleveland who talks like a guy who's been sober for three years and still thinks about drinking." Now the model has something to match instead of something to invent.

AI reconstructs your voice from memory (assuming you GIVE it your voice to add to memory). It doesn't copy it.

When I asked models to quote specific passages from a manuscript, about 30% of the "verbatim" quotes had drift. Word swaps. Pronouns changed. Phrases that sounded close but didn't actually exist in the source text. Some were wholesale fabrications.

That same drift happens when you ask AI to continue YOUR writing. It's not matching your voice precisely. It's generating what it THINKS your voice sounds like based on the sample you gave it. The longer the conversation goes, the more it drifts. You start a scene in tight, clipped prose. Before long the AI is writing flowing compound sentences with adjectives you'd never use.

The fix: re-anchor frequently. Paste a fresh sample of your actual prose every few prompts. Don't let the conversation run for 30 exchanges without reminding it what your writing actually sounds like. Treat the voice sample like a leash, not a one-time instruction.

AI has default opinions about prose. Know what they are.

Running analysis on dozens of manuscripts, I noticed the models have... habits. Pet observations they reach for regardless of whether they apply. "Show don't tell" gets flagged on passages that are ALREADY showing. "Vary your sentence length" appears even when the rhythm is genuinely strong.

This matters for generation too. When you ask AI to write a scene, it brings those same biases. It will default to "show don't tell" mode and write around direct statements that your story actually needs. It will vary sentence length for the sake of variety even when your style calls for deliberate repetition. It'll avoid adverbs like they're radioactive because that's what its training data says "good writing advice" looks like.

You're the author. If your style uses short declarative sentences, tell the AI that's intentional. If your narrator is the kind of person who WOULD use an adverb, say so. Otherwise the model quietly "corrects" your voice toward its idea of craft, and you end up with prose that sounds like everyone else's AI output.

When the model argues with itself, you get better scenes.

On the analysis side, I learned that asking an AI "is this finding correct?" is useless. It confirms everything. Always. Even fabricated findings. But asking it to argue AGAINST its own output? That produces genuinely useful pushback.

Apply this when you're writing. You draft a scene with AI help. Instead of asking "is this scene good?" (it'll say yes), ask: "What's the strongest argument that this scene doesn't work? Assume a tough developmental editor is reading it."

You'll get specific structural problems instead of cheerleading.

Then flip it: "Now argue that the scene DOES work and those criticisms are wrong." Whatever survives both passes is real. Whatever falls apart in the cross-examination was weak. You've just run a developmental edit on a single scene for the cost of two prompts.

Context windows are lying to you about capacity.

The specs say 100K tokens, 200K tokens, 1M tokens. And technically, that's true. But when I ran analysis on chapters near the end of a long conversation, the model was referencing details from early chapters that had already blurred. Character traits shifted. Timeline details contradicted earlier responses. The context was THERE in the window but the model's attention had faded.

For writing: if you're building a novel across a long AI conversation, the model is slowly forgetting your earlier chapters even before you reach the context window threshold. It'll keep generating, and the output will feel coherent sentence-to-sentence, but continuity starts leaking. Your blue-eyed character gets brown eyes in chapter 12. The promise set up in chapter 3 never pays off because the model doesn't remember making it.

This is a known LLM trait: It is strong on the beginning and end of a long context window, and spotty in the middle. Plenty of research confirms this.

Break your sessions into chapters. Start fresh for each one. Give the AI a brief that covers the story so far, the relevant character details, and the goals for THIS chapter. It's more setup work. The output is better.

Run the same generation prompt twice before you commit.

I discovered this on the analysis side and it applies directly to writing. Run the exact same prompt twice. Compare the outputs. The ideas that show up BOTH times are real observations the model is making about your story. The ideas that appear once and vanish were random. I actually run each prompt 3 times, then compare and if at least 2 of the 3 outputs don't match or come close, I throw the finding away altogether.

When you're generating scenes: if you ask for three possible directions for a chapter and one of them is genuinely interesting, run the prompt again. If that direction shows up again (even in a different form), it's probably grounded in something real about your setup. If it vanishes and you get three completely different options... that first suggestion was a coin flip, not an insight.

The model will always be more confident than it should be.

This was the single clearest lesson from the analysis work. When the AI is wrong, it's wrong with the same tone and certainty as when it's right. No hedging or "I'm not sure about this one." It uses the same measured, authoritative voice delivering a fabricated quote or a misread character arc.

When you're writing with AI, remember that. The model will commit fully to a plot direction that doesn't track. It'll write a scene with total confidence that contradicts your established world. It won't flag its own inconsistencies. That's your job. The model is a collaborator who never says "wait, are you sure?" so you have to be the one who does.

TL;DR version:

- Tell the AI your POV, tense, and character details before you ask it to write anything. Two sentences of context beats ten exchanges of correction.

- Re-paste a fresh sample of your prose every few prompts. Your voice drifts in the model's memory. Keep the leash short.

- Know the model's default opinions about "good writing." If your style breaks those defaults on purpose, say so, or it'll quietly sand your voice down.

- Ask the model to argue against its own output. "What would a tough editor say about this scene?" gets you real feedback. "Is this good?" gets you cheerleading.

- Start fresh for each chapter. Long conversations leak continuity. Brief the AI on the story so far instead of trusting it to remember.

- Run the same prompt twice (or three times) before you commit to a direction. Ideas that survive multiple passes are grounded. Ideas that vanish were coin flips.

- The model never says "I'm not sure." That's your job.

What's your experience been? Curious how others are handling the drift and consistency problems, especially on longer projects.

I built a manuscript analysis tool called FirstReader. The main product is a fiction craft analysis (319 principles from published craft books, chapter by chapter), but one of the features I'm most interested in right now is the free AI Perception Analysis.

Quick version: it scans your manuscript for the specific patterns that readers, editors, and reviewers associate with AI-generated writing. Repetitive sentence structures, filler phrases, paragraph shapes that show up constantly in LLM output. It's not a detector. It doesn't claim to know whether AI was used. It identifies the patterns and shows you where they are so you can decide what to do about them. Fully deterministic, no AI in the analysis itself, genre-aware baselines so romance conventions don't get flagged as AI tells in a romance manuscript.

It now works for both fiction and non-fiction. Free at firstreader.app. If you try it, I'd love to hear your thoughts on it.

The ask:

I'm building out the non-fiction analysis pipeline and I need beta manuscripts to test against. Specifically, I need:

- Narrative non-fiction - memoir, biography, true crime, narrative journalism

- Expository non-fiction - analytical, how-to, textbook, reference

I've already validated the pipeline on prescriptive non-fiction (self-help, instructional) and it performed well. But narrative and expository are structurally different enough that I need real manuscripts to make sure the analysis handles them correctly.

What I'm offering: a free full analysis report on your manuscript in exchange for your honest feedback on what the report got right and what it missed. Your manuscript stays private, never used for training, stored securely behind auth.

If you've got a non-fiction manuscript in either of those categories and you're curious what a craft analysis would look like on it, let me know either in a DM or a comment.

Most of us here are looking for beta readers to tell us what's working and what isn't. And betas are great at that. They tell you the middle felt slow, the dialogue went flat in chapter eight, the ending didn't land.

What they usually can't tell you is WHY. Was the slow middle a pacing problem? A scene that didn't turn? A POV shift that broke immersion? That's craft-level diagnosis, and it's not what betas are for. They're readers, not editors.

An alpha read is the step that goes before betas. Scene structure, pacing, show vs. tell, dialogue mechanics, narrative distance - all evaluated while the manuscript is still raw enough to fix without a full rewrite. The kind of analysis a developmental editor would do, except you'd have to wait 8 weeks and spend up to $4,000.

When you fix the craft-layer stuff FIRST, your betas can actually react to your story instead of tripping over structural problems. Their feedback gets more specific, more useful, and you're not spending months trying to decode what "the middle felt slow" actually means.

I ended up building a tool around this concept. It reads your manuscript against 319 published craft principles (McKee, Browne and King, Swain, Gardner) and gives you a chapter-by-chapter report with every finding traced to its source. Called FirstReader, launching soon.

Wrote a longer breakdown of the alpha reader concept here if you're curious: firstreader.app/blog/what-is-an-alpha-reader?src=reddit

Happy to answer questions about how it works or how it compares to just running your manuscript through a chatbot prompt.