There's a small ritual most creators know by heart. You finish editing at 1 a.m., hit upload, and YouTube shows you an empty description box. You stare at it. You type "In this video, I..." — delete it — type something else — delete that too.
On r/NewTubers, someone described their workaround: "adding the subtitles into the query for context and it generates... descriptions" — meaning they pull the auto-captions, paste them into an AI chat, and ask for a description (reddit_17fy762). Another thread asked the simpler question: "What do you personally put in your description?" The answers ranged from keyword soup to two-line summaries to "nothing, honestly."
That gap — between knowing the description matters and actually writing one at midnight — is why description generators exist. Most of them don't work well. Here's why, and what to do instead.
Why the description box is harder than it looks
The first 150 characters of your description appear in search results and next to the title on watch pages. Everything below the fold still gets crawled for keywords. YouTube's own guidance says descriptions contribute to search ranking — not as heavily as title and thumbnail, but enough that a blank description is a real handicap for search traffic.
So a good description has to do three jobs at once:
- Hook a browser in the first two lines
- Carry the keywords people actually search for
- Stay accurate to what's in the video
Job 3 is where most tools fail. And it's the one that matters, because a description that promises content the video doesn't deliver hurts retention, not just rankings.
The current options, and their failure modes
Write it manually. Best quality, worst economics. A decent description takes 10-15 minutes per video if you're disciplined. If you publish daily, that's over an hour a week on text nobody thanks you for.
Template copy-paste. "Hey guys, welcome back to my channel!" repeated across 200 videos. Viewers skim past it, and it carries zero video-specific keywords.
Paste the transcript into a general AI chat. This is the reddit_17fy762 approach. It works, sort of. Three problems:
- Context limits. A 30-minute video transcript runs 4,000-5,000 words. Long videos get truncated, and the model summarizes the first half.
- Hallucinated details. Ask for timestamps and chapters in the same pass and the model invents them. LLMs can't hear audio; they guess plausible-looking times like "02:45 Introduction" whether or not that's when the introduction happens.
- Generic output. The model doesn't know which parts of your video were the interesting parts. You get an even-weighted summary.
Dedicated description generator tools. SaaS products in this space charge $12-29/month. Many of them are a thin wrapper around the exact paste-transcript workflow above, with a nicer form. If you publish 4 videos a month, that's $3-7 per description.
The DIY path (no product needed, honest version)
If you want to do this properly for free, here's a workflow that holds up:
- Get the transcript.
yt-dlp --write-auto-sub --skip-download <url>pulls YouTube's auto-captions as a .vtt file. For better accuracy, run the audio through Whisper locally (whisper audio.mp3 --model large-v3) or an API. - Fix the proper nouns before generating anything. Auto-captions will mangle your product names, guest names, and technical terms. Two minutes of fixing here saves you from publishing a description with the wrong product name in it.
- Prompt with structure, not vibes. A prompt that produces usable output looks like:
"Here is a transcript with timestamps. Write a YouTube description: first 2 lines = a specific hook naming the concrete outcome or problem (under 150 chars total). Then 3-5 sentences summarizing what's covered. Then a keyword line: 5-8 search phrases a viewer would type. Only reference topics that appear in the transcript. Do not invent timestamps."
- Keep a personal template for the evergreen part — links, socials, disclosure — and paste the generated part above it.
Total time after setup: 5-8 minutes per video. The failure point is step 2. Do it for ten videos and you'll notice you're fixing the same ten misrecognized names every single time. That repetition is the tax that never goes down.
How postwriter.cn handles it
We built postwriter.cn around that exact repetition problem. Upload the video; you get the transcript, a description draft, chapter timestamps, title options, social copy, and an SRT file in one pass.
The part that matters for descriptions is the review page. The transcript is aligned to the audio word by word, so you click any wrong word, fix it, and the fix goes into your personal dictionary. If the engine misheard "Vercel" as "for sell" in video 1, you fix it once, and in videos 2 through 50 it's already right. The system converges instead of making you re-correct the same names every upload.
Descriptions are generated from that corrected transcript, so what's in the box matches what's in the video. Timestamps are read from the audio alignment, not guessed. It's free during beta, and the founder price after that is $39 for 3 years — priced against the reality that this is a 5-minute task, not a $29/month task.
FAQ
Does the description affect YouTube SEO?
Yes, modestly. Title, thumbnail, and watch time matter more, but descriptions are crawled for keywords and the first 150 characters show in search. A blank or generic description leaves ranking signal on the table.
How long should a YouTube description be?
200-400 words is the practical range. The first 2 lines do the CTR work; the rest carries keywords and links. Past ~500 words, you're writing for robots that don't reward it.
Should I put keywords as a comma list at the bottom?
A short natural phrase list is fine and common. A 500-tag wall looks like spam to viewers who expand the description, and YouTube has said keyword stuffing in descriptions is treated as spam.
Can't I just use ChatGPT for free?
You can, and the DIY section above is exactly that workflow done carefully. What you're paying a tool to remove is the transcript-wrangling, the timestamp hallucinations, and the repeated proper-noun fixes. Whether that's worth it depends on your volume.
Does it work for languages other than English?
The current beta focuses on English content, including heavily accented English — that's covered in detail in the next section of this series.
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