How to write a social post with a neural network: approach, prompts and a checklist

What a neural network can do for a post, and what it can’t

Task	Can a neural network help	What stays with you
Ideas and post topics	Yes: it will suggest options by category or by an audience question	Choosing the topics your readers and your business actually need
A draft from a brief	Yes, if the brief is specific	Giving the inputs and editing the result
Headlines and opening lines	Yes: several options to choose from	Choosing one and polishing it
Shortening and adapting for a platform	Yes	Checking that the meaning and facts survived
Changing the tone	Yes, especially if you show an example	Checking it against the brand’s tone
Fact-checking	No: the model itself can be wrong	Checking numbers, names, dates and terms against the source
Knowledge of your product, prices and terms	Only from what you gave it	Providing current data
Recent events	Limited: the model’s knowledge is most complete up to a certain date	Checking that the information is current

The main takeaway from the table: a neural network handles the form of a text well, but you’re responsible for the content. The model doesn’t know your delivery terms changed or that you launched a new service unless you told it.

The same goes for online post generators. They usually have a field for the topic and a few settings. If you don’t give the generator a goal, an audience and facts, it has nowhere to get them from, and the text comes out generic.

What to prepare before asking a neural network to write a post

The quality of a draft depends on the inputs far more than on the phrasing “write a post.” Before you open the model, gather:

• The post’s goal. What the reader should understand or do: book a call, ask a question, learn about a new service.
• The audience. Who’s reading and what matters to them.
• The platform and format. Where the post will run and in what shape: a short note, a detailed breakdown, an announcement.
• One main point. If you can’t put it in a single sentence, the post isn’t about anything yet.
• Facts. Numbers, dates, terms, links — only verified ones, from your own materials.
• The brand’s tone. How you address the reader, which words you avoid. Best of all is an example of a post that worked well.
• Constraints. What can’t be written: promises you don’t make, comparisons with competitors, internal information.

This is the actual brief for the post. If posts go out regularly, it’s convenient to keep topics and goals in one document. There’s a separate breakdown of how to put together a publishing plan.

How to write a post with a neural network: step by step

Model developers, in their own prompting guides, describe similar elements of a good brief:

• Role — what capacity the model acts in, for example a company’s social media editor. The role helps set the scope of the task and the tone of the answer.
• Task — a clear statement of what you need to get.
• Context and inputs — information the model doesn’t have: about the company, the product, the audience.
• Format — length, structure, the shape of the answer.
• Examples — a sample text to use as a reference. Examples influence the format, tone and structure of the answer.
• Constraints — what not to do. It helps to explain why a constraint is there, so the model understands your goal better.

This isn’t a formula with a guaranteed result. A model’s answers are non-deterministic: the same prompt can produce different texts. That’s why the work runs as a few short iterations — repeated requests with refinements.

1. Write the brief using the elements above. Start with the template in the next section.
2. Provide context and facts. Paste verified data into the brief and ask the model to use only that.
3. Ask for two or three variants with different approaches: for example, opening with a reader’s question, with an example, or with a takeaway.
4. Give specific feedback. Not “make it better,” but “shorten the second paragraph by half” or “drop the informal address.” Developers recommend analyzing the answers and refining the prompt based on them.
5. Edit by hand. Especially the first and last sentences: they do the most to decide whether the post gets read to the end and what happens afterward.
6. Check the facts against the checklist below.
7. Adapt for the platform: length, paragraphs, links, hashtags.
8. Approve and publish. The decision to publish is made by the person responsible for the channel.

Prompt templates for posts

The templates below are a starting point. Replace the bracketed fields with your own data and delete lines you don’t need. Save the templates that worked — over time you’ll build your own library of briefs.

A universal post template

You are the social media editor for [company name and what it does].
Task: write a post for [platform].
Goal of the post: [what the reader should understand or do after reading].
Audience: [who's reading and what matters to them].
Main point: [one point, one sentence].
Facts you can use: [list of verified facts, numbers, links].
Tone: [e.g., calm and to the point, address the reader formally].
Example of our post to use as a reference: [text of a post that worked well].
Format: [length, number of paragraphs, whether a call to action is needed].
Constraints: use only the facts from the list. If data is missing, say exactly what's missing instead of making it up. Don't use [words and phrases that shouldn't appear].

The last line matters. One model developer’s documentation recommends explicitly allowing the model to admit information is insufficient, and limiting it to the materials provided — this reduces the number of invented details.

An expert post: explaining something complex simply

Explain [complex topic or term] for [audience] who aren't familiar with it.
Rely only on these materials: [expert's key points, excerpts from documents].
Structure: the first sentence answers the question "[reader's question]"; then a real-life example of [what situation]; end with one practical tip.
Explain terms in plain words the first time they come up.
Length: [target].
If the materials don't answer some question, don't guess — flag it instead.

Good for posts where the company shares expertise. Show the finished text to the expert: simplifying easily turns into inaccuracy.

A company news item or announcement

Write an announcement for [platform]: [what's happening — an event, a launch, a change].
Facts: date — [date]; location or format — [address / online]; audience — [who]; how to take part or where to find out more — [link or action].
Start with what matters most: what changes for the reader.
Tone: [tone]. Length: [target].
Don't add anything that isn't in the facts.

In an announcement, a mistake in the date or the link is the most expensive one. Double-check those separately, even if the rest of the text didn’t need editing.

An ad post

Write an ad post for [platform] about [product or service].
Audience: [who they are and what they need].
Why the offer is useful: [benefits you can back up].
Terms: [price, deadlines, restrictions — if you state them, state them precisely].
Call to action: [what to do and where].
Tone: [tone]. Length: [target].
Don't promise anything that isn't in the facts. Don't use superlatives or comparisons with competitors unless you can back them up.

For an ad post, checking is especially important. Under advertising law, an ad must be fair and accurate. Online advertising must carry an “ad” label and identify the advertiser or a site with information about them. So check every promise and every term against the actual offer.

A post for VK and for Telegram

Adapt this text for [VK / a Telegram channel]: [source text].
Requirements: length — [target]; paragraphs — [short / same as the source]; emoji — [none / moderate]; hashtags — [needed, and which ones]; link — [where it leads and where it sits in the text].
The first line should say right away what the post is about.
Keep the meaning and all the facts; don't add anything.

The same text reads differently on different platforms, so it’s better to state the platform’s parameters explicitly rather than hope the model guesses them. Check length and formatting limits in the platform’s own help center before publishing — they change. If the post will run as a promoted entry on VK, there’s a separate breakdown of how to promote a post through VK Ads.

A post series and content-plan ideas

Suggest [number] post topics for [platform] for [period].
Company: [what it does]. Audience: [who's reading and what questions they have].
Categories: [e.g., tips, answers to customer questions, company news].
For each topic, give the category, the post's goal, the main point in one phrase, and the format.
Don't repeat topics from the list of ones already published: [list].

A neural network quickly produces a lot of topics, but some of them will turn out generic or a poor fit. Choosing the topics and scheduling them is your job.

A prompt for editing a finished text

Below is a draft post. Don't rewrite it completely. Find and list:
— clichés and generic phrases;
— repetition;
— sentences that are too long;
— numbers, facts and claims that need checking;
— places where it's unclear what the reader should do.
For each point, quote the fragment and suggest a fix.
Draft: [post text]

Asking it to quote fragments makes the answer verifiable: you can see exactly which spot each note refers to. This prompt also works on text written without a neural network. Which edits to accept is still your call.

An editing checklist for a post after a neural network

Go through these points before every publication:

1. Numbers, dates, names, prices and terms are checked against the source. Anything that can’t be verified has been removed.
2. The text has no invented links, quotes, studies or experts.
3. The post serves its goal and covers one main point.
4. The tone matches the brand’s tone: no corporate jargon, clichés, or an address you don’t use.
5. The first sentence gets straight to the point, with no windup.
6. One idea per paragraph, and the paragraphs are short.
7. The call to action is specific and honest: the reader understands what happens after they click.
8. The format fits the platform: length, links, hashtags, formatting.
9. An ad post is accurate and labeled.
10. The text has no customer personal data and no other people’s text you aren’t allowed to use.
11. The final text has been read and approved by the person responsible for the channel.

If you end up rewriting more than half the text at some point in the checklist, go back to the brief — you probably left out some inputs.

Limitations and risks of neural networks

Confident mistakes. Language models sometimes generate text that’s factually wrong or inconsistent with the context they were given. These mistakes are called hallucinations. A model’s answers can look logical and convincing while still being made up. Prompting techniques reduce how often this happens, but they don’t eliminate it entirely, so important information needs checking.

Limited knowledge. For example, one developer’s documentation gives, for each model, the date up to which its knowledge is most complete and reliable. The model may not know about later events, or may know them only partially. Even if a service can search the internet, check where the information actually came from.

Inconsistent results. The same prompt can produce different answers. Models get updated regularly, so a prompt that used to work well is worth retesting from time to time.

Handling of your data. Terms vary between services. One developer’s documentation says that request and response data may be retained by default to improve the service, and recommends turning off request logging when sending personal or confidential data. For another, using chats to improve models in consumer products depends primarily on the user’s own settings. Before pasting customer data, commercial information or internal documents into a neural network, read the service’s terms and clear it internally. Never paste passwords or access credentials into a prompt.

Sameness. Similar briefs produce similar texts. If every post is built from the same template with no facts or examples of your own, a feed quickly starts to look identical post after post. Real-life details help: cases from your practice, customer questions, a specialist’s opinion.

How to build a neural network into a regular posting schedule

A one-off post can be written without a system. If posts go out every week, it helps to agree on an order:

1. Publishing plan — topics, goals and dates. Approved by the person responsible for the channel.
2. Post brief — the inputs from the checklist above. Prepared by the author or editor.
3. Draft — produced by the neural network from the brief.
4. Editing — facts, meaning, tone. Done by the editor.
5. Approval and publishing — only a checked text. Decided by a person.
6. Reviewing results — which topics and formats worked. Takeaways carry over into the next plan.

In this setup, the neural network is responsible for only one step — the draft. The rest stays with people, and that’s as it should be: they’re the ones who make posts useful and recognizable. We’ve described a similar order for our own social media work — see how the setup works where AI prepares a draft and a person approves it.

Once there’s a lot of material and several channels running, the process is worth describing in more detail: roles, documents, checkpoints. There’s a separate article on how AI-powered content production runs at scale.

Social posts and website copy are different genres — articles on a site follow their own logic, tied to search demand. If you need both, see how to prepare website copy for search queries.

Frequently asked questions

Which neural network writes the best posts?

There’s no universal answer: models and services keep updating, and quality depends on the task. Compare them by criteria: how well the model writes in your language, whether it can work with your materials, what the service’s data-handling terms are, how convenient it is to refine the result, and what it costs. The simplest approach is to give the same brief to two or three options and compare the drafts.

Can a post from a neural network be published without editing?

You shouldn’t. A model can confidently get facts wrong, and unedited texts quickly start sounding the same. Even if a draft looks finished, run it through the editing checklist.

How do I keep a post from sounding “like it’s from a neural network”?

Give the model your own facts and examples, and attach a sample of your tone to the brief. After generating, rewrite the first and last paragraphs by hand and remove clichés: generic intros, exclamation marks, phrases that would fit any company.

Can a neural network write a post from a photo?

Some models accept images as input: for example, one developer’s documentation states that all of its current models support text and image input. Check that the description matches what’s actually in the photo: the model can misread details, and it won’t know prices, specs or names from a photo if they aren’t visible in it.

What should I do if a neural network made up a fact or a link?

Delete it or replace it with a verified one. To lower the risk, state in the brief that only facts from your list can be used, and allow the model to say when data is missing. Open links yourself before publishing.

AI can speed up drafting, but the regularity and quality of posts still rest on the process and the editor.