# LinkedIn maxxing with ChatGPT: 12 prompts that turn it into a content system

> LinkedIn maxxing with ChatGPT: 12 prompts and the project setup behind them, from a voice guide and customer research to drafts, editorial review and reporting.

Source: https://linkedinlikes.com/blog/linkedin-maxxing-with-chatgpt · Updated: 2026-11-12

## Key takeaways

- Load your business context into a ChatGPT project first; project instructions take priority over your general custom instructions.
- Twelve prompts cover a voice guide, customer research, trend research, outlier analysis, transcripts, a calendar, drafts, review, stuck deals, conversations, reporting and feedback.
- Use Chat for drafting and Work for files such as calendars, spreadsheets and reports.
- Custom GPTs are being retired on December 11, 2026, so build repeatable steps as skills.
- Keep publishing, comments, connection requests and messages in your own hands.

LinkedIn maxxing with ChatGPT means using ChatGPT for the groundwork behind a good post, such as understanding customers, finding an angle, collecting evidence and checking what you can credibly say, and leaving the final argument to you. Asking it for "a LinkedIn post about X" skips exactly that groundwork, which is why such posts read as empty.

The 12 prompts below turn the groundwork into a routine you can repeat, from researching your market to planning a content calendar and finding which posts led to real business conversations.

By the end you will have:

- A voice guide built from the way you actually write and speak.
- A research routine that finds good ideas before the calendar asks for them.
- A drafting and review process with clear quality standards.
- A reporting loop that tells you what to write next.

ChatGPT fits this work because a single project can hold your instructions, files and linked Google Drive folders, and Work can turn a finished brief into a document or spreadsheet you can edit.

Keep this page for your next content planning session.

## What should you give ChatGPT before the first prompt?

Your job title and a request to "sound human" are not enough. ChatGPT needs to know who you sell to, what you believe and which claims you can back up.

Start by pasting this:

> Treat what follows as the standing context for my LinkedIn content. Turn it into a reference document I can store as a source in this project, and tell me which parts should go into the project instructions. Business: [company name, website, product or service, how we make money, usual sales cycle]. Audience: [customer roles, company sizes, industries, locations]. Their biggest problems are [problems], the objections they raise most are [objections], and the events that send them looking for help are [triggers]. Positioning: we help [audience] reach [outcome] by [approach]. What sets us apart: [differences]. We are the wrong choice for [disqualifiers]. Viewpoint: I hold these three opinions about my industry: [opinions]. I disagree with [common advice] because [reason]. Evidence: draw only on these approved case studies, results, examples and personal experiences: [sources]. Never make up customers, revenue numbers, stories or quotes. Voice: these [10-20 posts] and [transcripts or writing samples] sound like me. Stay away from [phrases, tones, formatting habits]. Goals: my main LinkedIn goal is [qualified conversations / deal support / recruiting / awareness]. My offer is [offer], and I want interested readers to [action]. Working rules: use connected sources only where you have access and permission. If you can't open a source, ask me for an export or pasted text. Publishing, comments, connection requests and messages on LinkedIn stay with me. Put drafts in [review destination], keep evidence apart from inference, and flag anything that needs my input.

Update that context whenever your offer, audience or opinions change.

### Where does ChatGPT keep this context?

Open the left sidebar, choose New project and give it a clear name, such as "LinkedIn content". A project groups chats, files and instructions, and every new chat inside it starts with the same background. Copy the working rules from the prompt above into the project instructions, because inside a project they override your general custom instructions. A short version might say: "For every LinkedIn request in this project, use the business context and voice guide sources. Mark any claim without a source as [needs evidence]. Never write as though a post has already gone live."

Store the material itself in the project's Sources. You can upload files, paste a link to a Google Drive file or folder or a Slack channel, add a quick text note, or save a good ChatGPT answer into the project so it becomes reusable knowledge. In the desktop app, a project can also be tied to a folder on your computer, which suits writers who keep drafts as local files.

Choose the memory setting deliberately. Default memory lets the project use everything ChatGPT remembers about you. Project-only memory keeps the project sealed off, which is tidier for client work, but ChatGPT Work is unavailable in project-only projects, and shared projects always use project-only memory. If you want Work for calendars and reports, stay on default memory.

Two other places matter. Settings > Personalization holds custom instructions and memory, so rules that apply to every chat, like phrases you never use, belong there. Once the process is settled, turn it into a skill (Plugins > Skills, using the built-in skill-creator where your account has it). Custom GPTs are being retired on December 11, 2026, so do not start a new one for this.

Last, back the "publishing stays with me" rule with an actual setting. Under Settings > Plugins, every connected app has a permission level: Always ask, Allow read actions or Allow low-risk actions. For the apps this workflow reads from, Allow read actions is enough, and ChatGPT will still check with you before anything that changes data.

## Part 1: research and content production

Prompts 1-8.

## 1. How do you build a voice guide with ChatGPT?

A useful voice guide explains the decisions behind your writing: how you introduce an argument, use examples and move from one idea to the next.

A label like "conversational and authoritative" does not help anyone write.

> Study the posts and transcripts I've attached and build a practical voice guide for my LinkedIn writing. Describe how I open a post, explain an idea and close an argument; my usual vocabulary, sentence length, paragraph length and use of lists; how I show disagreement, doubt, humour and confidence; and the phrases I use naturally, quoted word for word with a source reference. Note habits that show up in my weaker examples so they don't become rules. Add a list of phrases and patterns to avoid, including needless repetition, invented drama, certainty I can't support and too many one-line paragraphs. End with three before-and-after edits that turn generic copy into my voice without changing what it says. Make the guide concrete enough for another writer to follow.

**In ChatGPT:** run this inside the LinkedIn project. One message can carry up to 20 files, so export your best posts plus two or three transcripts and send them together. Add a couple of weaker posts on purpose and label them, so the guide can tell habits worth keeping from habits to drop. When the guide looks right, save the answer into the project as a source, and every later chat in the project can cite it. To change part of the guide later, edit it in the chat's writing block: select the section and ask for a change to that part only. Then ask ChatGPT what it remembers about you and delete any old memory that contradicts the new guide, so it does not creep back into drafts.

**Why it matters:** a good voice guide makes your feedback reusable. You stop fixing the same phrasing every week and improve the instructions behind every draft.

## 2. How do you find the customer questions worth answering?

Sales calls and customer conversations bring up questions no keyword tool will show you. The best ones usually sit around a decision the customer finds hard.

> Go through the sales calls, support threads, customer interviews and closed-lost notes I've supplied from [period]. Sort the questions that keep coming back into five groups: problems customers are trying to make sense of, worries that slow a decision, alternatives they are weighing, expectations that end in disappointment, and questions they ask just before they sign. For every theme, give one anonymised quote, its source, how many separate conversations mention it, the customer's role and their stage in the decision. Suggest two LinkedIn angles per theme, one that helps readers diagnose the problem and one that helps them judge a solution. Rank the ten strongest by how often they recur, how much they matter commercially and how good our evidence is. Keep what customers said apart from your interpretation.

**In ChatGPT:** if the call notes sit in Google Drive or Slack, connect those apps under Settings > Plugins and bring them into the chat with @ (or choose them from the + menu). ChatGPT may ask before reading; check the request and approve it. For a large archive, switch the message box from Chat to Work and ask for a spreadsheet of themes with one row per quote. If the notes contain customer names, ask ChatGPT to replace them with roles before it builds the table. Work hands back a file you can sort and check, which is easier to audit than a long chat reply. Deep research can also treat connected apps as trusted sources, which helps when notes are split across Google Drive and Slack.

**Why it matters:** you get a backlog of topics tied to conversations your company is already having, in language you recognise and with evidence you can verify.

## 3. How do you spot topics moving from X into your market?

A debate picking up on X can become a strong LinkedIn post, as long as you explain why it matters to your customers and add something beyond a summary.

> Using web search, deep research limited to the sites I name, and the X posts and threads I link or paste, review the past [seven days] of discussion about [topics] and from these accounts: [accounts]. Find up to 10 developments or arguments that matter to [customer]. For each, give the original source, author, date and link; a neutral summary of what happened or what people are arguing; the strongest competing readings; what it means for our audience day to day; and an angle we could support with our own experience or evidence. Treat reaction counts as a snapshot, do not take popularity as proof, and check factual claims against the original source where you can. Recommend three ideas to develop and say what I would need to add before each one becomes a credible LinkedIn post.

**In ChatGPT:** ChatGPT can search the web, but none of the OpenAI documentation we checked mentions a built-in X search, so handle X like any other website. Start deep research from the + menu or the sidebar, edit the research plan before it runs, and limit it to domains you trust; adding a line such as "skip sites that only repost the original" to the plan helps. It shows progress, lets you change direction while it runs and produces a cited report you can download as Markdown, Word or PDF. For posts a search will not find, paste the links yourself. In the desktop app, the built-in browser can open the pages you send it to. Information from connected apps can also shape the queries ChatGPT runs on the web, so links your team drops into a connected Slack channel give the search better starting terms.

**Why it matters:** research gives you somewhere to start, and your judgment gives readers a reason to follow you. A good adaptation ties the debate to a decision your readers really face.

## 4. How do you analyse posts that beat their author's usual results?

Measuring every post against a famous account's biggest hit skews your research. First compare a post with what its author usually gets, then look at what might explain the gap.

> Analyse the LinkedIn swipe file I've attached along with whatever performance data it has: [file]. Where the data allows a fair comparison, set each post's reactions, comments and reposts against the author's median for a similar format and period. Show the calculation, and flag small samples, missing metrics and very recent posts. For the clearest outliers, describe the topic and intended reader; the opening, argument, evidence and close; the format and rough length; whether the discussion under it suggests real customer interest; and possible confounders such as a launch, a news event or unusually wide distribution. Turn this into five reusable structural patterns, then suggest one original idea for each based on my own experience. Do not copy distinctive wording, and do not present a pattern you observed as a rule of the algorithm.

**In ChatGPT:** attach the swipe file as a CSV or spreadsheet, since screenshots will not let ChatGPT compute medians and show its working in a table you can recheck. Tell it in the message which columns hold reactions, comments and reposts, so no median is calculated from the wrong field. If you want to reuse the calculation next month, ask for it as a downloadable sheet, and keep the raw export in the project Sources so the next run starts from the same baseline. For a quick check on a single post, our [LinkedIn engagement rate calculator](https://linkedinlikes.com/linkedin-engagement-rate-calculator) gives you a figure to set against the author's median.

**Why it matters:** you end up with hypotheses worth testing, and you avoid assuming that copying a successful layout will copy its results.

## 5. How do you turn recorded know-how into original posts?

A transcript can hide a strong post inside a story, a throwaway explanation or a disagreement that deserved more attention than it got at the time.

> Read this interview, podcast, webinar or meeting transcript: [source]. Find eight moments that could carry an original LinkedIn post. Favour decisions, mistakes, surprising observations, practical explanations and opinions grounded in experience. For each moment, give the exact quote with its timestamp or location; the main idea and who would gain from it; any context needed so the speaker is not misrepresented; facts we already have that support it; and one follow-up question that would make the idea stronger. Choose the three best moments and draft a post from each in my voice guide. Keep the speaker's level of certainty, strip out confidential details, and leave placeholders wherever a result or example still needs confirming.

**In ChatGPT:** upload the transcript, or mention Google Drive with @ and name the file. The voice guide already sits in the project as a source, so you only need to say "use the voice guide in this project". Ask for the eight moments first and the drafts in a second message, with timestamps copied exactly from the file so you can jump to that spot in the recording. Checking the quotes before any writing starts makes a misread timestamp easier to catch. Scan client recordings for names before uploading: on Business, Enterprise and Edu plans your content is not used for training by default, while on personal plans that depends on your "Improve the model for everyone" setting.

**Why it matters:** you can publish from knowledge that already exists, without asking someone to come up with a fresh opinion every time the calendar has a gap.

## 6. How do you build a four-week content calendar?

A calendar should weigh what customers need to understand against what you have enough evidence to say. Filling every slot with another version of your offer makes your feed repetitive.

> Plan four weeks of LinkedIn posts at [three posts a week] from the ideas we approved in research. Mix practical explanations that help customers solve a problem, opinions backed by experience or evidence, customer examples we have permission to use, answers to objections and late-stage questions, and the occasional offer post with a fitting next step. For every post, state the reader, the one goal, the main argument, the source material, the suggested format and what the reader should do afterwards. Check for repeated topics, overlapping arguments, unsupported claims and too many promotional posts. Write full briefs for week one and shorter outlines for weeks two to four. Leave one slot a week open for something timely, since the calendar does not have to stay fixed.

**In ChatGPT:** this is a good first job for Work. Ask for the calendar as a spreadsheet and the week-one briefs as a document, then review both in the file viewer. Add a column naming the source file behind each post, so every brief stays tied to its evidence. If teammates need to comment, put the calendar on a page in Space and share it with View, Comment or Edit access. Before you lock the posting slots, look up [the best time to post on LinkedIn](https://linkedinlikes.com/best-time-to-post-on-linkedin) for your audience, and ask Work to mark which slots are fixed and which one each week stays open. When a brief calls for a carousel, outline the slides in [our carousel maker](https://linkedinlikes.com/linkedin-carousel-maker). If the plan changes mid-month, ask for a revision in the same Work chat so the earlier briefs stay in context.

**Why it matters:** a good calendar cuts weekly planning time and still leaves space for better ideas, new evidence and changes in the market.

## 7. How do you get drafts that actually say something?

Your research should settle the argument before ChatGPT starts polishing sentences. Skip that and you get tidy prose that takes several paragraphs to state the obvious.

> Write three LinkedIn drafts from these approved briefs: [briefs], using my business context and voice guide. In each draft, make one clear argument for one intended reader. Open with a specific observation, tension, result or question that the brief supports. Explain the reasoning through a concrete example. Use bullets where they make parallel items easier to scan. Vary sentence and paragraph length, with the odd one-line paragraph for emphasis. Close with a useful implication or a natural next step. Aim for [180-300 words] unless the idea needs a different length. Avoid repeating the ending, made-up personal stories, inflated promises and formulaic contrasts. Give two alternative openings and say briefly how each shifts the emphasis. List missing evidence separately from the copy that is ready to publish.

**In ChatGPT:** use Chat here and leave Work aside, because you want a discussion about the argument more than a finished file. Every draft appears in a writing block, so you can select one paragraph and ask for a fix without regenerating the rest. Ask for each draft in a separate block, which also makes it easy to copy one without the notes around it. If neither alternative opening convinces you, run the brief through our [LinkedIn hook generator](https://linkedinlikes.com/linkedin-hook-generator) and paste the best line back for ChatGPT to build on. The project instructions already hold your rules, so the drafting message can stay short: the briefs plus "use the voice guide source".

**Why it matters:** clear constraints produce drafts that are easier to judge, so your review can focus on the argument instead of cutting filler.

## 8. How do you set up a separate editorial review?

Give the reviewer one clear job: find the weak spots in a draft and explain how to fix them. A comforting "This is strong" is no editorial process at all.

> Review these LinkedIn drafts against the brief, the source material, my business context and my voice guide. Score each out of 100: relevance to customers up to 25, specificity and evidence up to 25, strength and clarity of the argument up to 20, match with my voice up to 20, readability and formatting up to 10. Show what each score is based on. Check every factual claim, mark generic passages, and flag repetition, abrupt one-line fragments and paragraphs that would read better as lists. Return a revision ready to publish, a short note on the changes that matter, and any open questions. Treat invented evidence, exposed confidential information or a misrepresented source as a blocker whatever the score. Keep everything in the review document until a human approves it.

**In ChatGPT:** start a new chat in the same project for the review. The reviewer then sees the brief, the sources and the voice guide, but not the conversation that produced the draft, so it is less inclined to defend its own choices. If you use this rubric every week, save it as a skill so the scoring stays consistent; eligible accounts can start one by typing @skill-creator and pasting the rubric. Before publishing, paste the revision into our [LinkedIn post analyzer](https://linkedinlikes.com/linkedin-post-analyzer) and check the length with the [character counter](https://linkedinlikes.com/linkedin-character-counter). Keep the scored version in the review chat, so prompt 12 can set the before and after next to each other.

**Why it matters:** a rubric makes your standards explicit. The score organises the feedback, and the source checks plus your editor's judgment decide whether the post is ready.

## What should happen in the first hour after you publish?

Everything above comes back to one moment: the first hour after the post goes live, when you want as many people as possible to read it and react.

**Start with people you can reach.** Colleagues, friends, relatives and clients: anyone you can actually contact. Ask them to read the post and react while it is new.

**If you have nobody to ask, use a community where real members act by hand.** The groups below are ones we checked for other articles, and they are a reasonable place to start.

**LinkedInLikes is our own free community.** Members like, comment on, repost and save each other's posts by hand from their own LinkedIn accounts. You sign up with email, get 100 starter points for your own posts, paste a post link and choose the action. Comments are written by members in their own words, and nobody asks for your LinkedIn password.

[Get comments on your post with LinkedInLikes](https://linkedinlikes.com/linkedin/comment)

## Which LinkedIn groups can you join for first-hour support?

These free communities were checked on Oct 21, 2026, and the member numbers are what each platform showed on that day. Each group has its own admins, so read the pinned message before your first post.

| Group | Platform | Members | Language | Good for |
| --- | --- | --- | --- | --- |
| [LinkedIn Engagement Pod & Group](https://t.me/linkedin_engagement_pod) | Telegram | 1,833 members | en | an all-round LinkedIn pod for likes and comments |
| [LinkedIn Engagement Pod (RVision Digital)](https://t.me/linkedinengagementpod) | Telegram | 1,064 members | en | sharing posts for likes and comments, connections and skill endorsements |
| [Linkedin engagement pod](https://t.me/linkedinpod1) | Telegram | 488 members | en | an all-round LinkedIn pod |
| [Social Linkedin/FB/IG Engagement Pod](https://t.me/socialengagementpod) | Telegram | 77 members | en | a LinkedIn pod where you react to and comment on the last 5 posts before you share yours |
| [LinkedIn For Breakfast](https://www.skool.com/linkedin-for-breakfast) | Skool | 6,603 members | en | a free LinkedIn community with pods and live events |
| [Linkedin Likes and Engagement](https://t.me/linkedinlikesandengagement) | Telegram | 697 members | en | swapping likes and comments, plus new connections |
| [Linkedin Pod (SF founders like-for-like)](https://chat.whatsapp.com/DBOyIPrDR5IK27yzW606sF) | WhatsApp | n/a (WhatsApp does not show the count) | en | founders trading likes on LinkedIn |
| [r/linkedintips](https://www.reddit.com/r/linkedintips/) | Reddit | 38,314 members | en | advice on getting your brand noticed on LinkedIn |
| [LinkedIn Club](https://t.me/linkedinclub) | Telegram | 552 members | en | networking and new connections on LinkedIn |
| [r/LinkedInGrowth](https://www.reddit.com/r/LinkedInGrowth/) | Reddit | - | en | growing a LinkedIn account and adding connections |

If you would rather skip the group search, [LinkedInLikes](https://linkedinlikes.com/linkedin/like) lets you post a link and get likes and comments from members who act by hand.

## Part 2: pipeline and performance

Prompts 9-12.

## 9. How do you write about deals that got stuck?

Some of your most useful posts may answer a worry that several prospects raised in private. The aim is to explain the issue so clearly that future customers arrive better informed.

> Go through the CRM records and call notes I've supplied for qualified deals that got stuck during [period]. Find the objections that recur and separate three kinds: objections stated outright in conversations, possible explanations nobody has confirmed, and operational delays that no post will fix. For each repeated objection, note the customer's role, the deal stage, the evidence we have and the question a useful LinkedIn post should answer. Propose five content briefs. Each should present the concern fairly, show where our approach fits, admit where it does not and name the proof we need. Draft the two strongest posts without naming prospects or exposing deal details. Separately, list approved resources a salesperson could share in a relevant conversation.

**In ChatGPT:** look under Settings > Plugins to see whether your CRM is available as an app; that depends on your plan, region and workspace, and an admin may need to switch it on. If it is not there, export the stuck deals to a spreadsheet and attach it. Set the app's permission to Always ask or Allow read actions for this job, because nothing here should change a record. On Business, Enterprise and Edu plans, data from apps is not used for training by default. Ask ChatGPT to cite the record or note behind each objection, so you can open the source before a brief goes on the calendar.

**Why it matters:** this links marketing to the questions salespeople keep having to answer, and it stops you writing persuasive posts about problems customers do not care about.

## 10. How do you prepare replies to the conversations your posts start?

When someone responds to a post, the right next move depends on what they said and whether a business conversation would be welcome. A like or a comment alone does not prove business interest.

> Read these LinkedIn conversations I've supplied: [comments, inbound messages or interactions I noted by hand]. Put them into four groups: questions that deserve a useful public answer, conversations where sharing a resource would help, clear interest in our offer, and general discussion with no business next step. For each relevant conversation, sum up what the person said they need, the business context we have and what we still don't know. Draft a reply that deals with their actual message before bringing up anything else. Where it fits, suggest one follow-up question or one resource we already have. Do not read business interest into a like, pretend to know someone, or push a meeting request into a conversation that doesn't call for one. Keep every reply as a draft for me to check and send.

**In ChatGPT:** paste the comments or a copy of the message thread, since ChatGPT can only work from what you give it here. Ask for the four groups as a table with one row per person, and keep this job in its own chat inside the project so personal details stay out of your drafting chats. If a thread contains details you do not want stored at all, use a temporary chat, which is not saved to history or memory. Move the finished drafts back into the project only after removing anything personal. For a public reply in a different tone, try the [LinkedIn comment generator](https://linkedinlikes.com/linkedin-comment-generator) and compare it with ChatGPT's version before writing your own.

**Why it matters:** preparing replies makes them more thoughtful without turning every exchange into a sales sequence. Sometimes the right result is simply a helpful answer.

## 11. How do you report on business results honestly?

Impressions show you how far a post spread. They cannot show whether the right people became interested or whether a post helped close a deal.

> Combine the LinkedIn analytics, tracked website visits, inbound enquiry records and relevant CRM data I've supplied for [period]. Report on reach, reactions and comments by post, topic and format; qualified inbound conversations; resource sign-ups and demo requests where we track them; opportunities with a documented LinkedIn touch; and the time spent producing and reviewing content. Keep directly tracked conversions, self-reported discovery and possible influence apart. Do not attribute a whole deal to LinkedIn because one contact reacted to a post. Mark the posts that reached relevant customers, the ones that reached a wide but less relevant audience, and the ones with too little data to judge. End with three findings, three limitations and one experiment to run next. Where you can, compare posts at the same age so new posts are not judged against older ones.

**In ChatGPT:** give this to Work with the LinkedIn analytics export and the CRM file attached, and ask for a short report plus the spreadsheet behind it. Ask that every figure trace back to a row in the file, and that the report open with a brief methods note covering which exports, which date range and which posts were left out and why. For posts with gaps in the export, our [LinkedIn impressions calculator](https://linkedinlikes.com/linkedin-impressions-calculator) gives you a consistent estimate to log. If some evidence lives on public sites, such as a competitor's published case study, use deep research; when every input is your own file, Work is the better fit.

**Why it matters:** better reporting gives you a reason to keep or change an approach, and it keeps uncertainty visible instead of turning patchy tracking into a confident revenue claim.

## 12. How do you turn your edits into a better system?

Each draft you edit tells you something about your standards. Record the lessons that keep repeating and you will spend less time fixing the same problems.

> Compare the original drafts, my edits, the versions I published and whatever performance data we have for [period]. Find the voice corrections I keep making, claims that needed more evidence, ideas that got clearer once they were framed differently, formatting changes that made posts easier to read, and topics that led to useful customer conversations. Propose updates to the voice guide, the research briefs and the editorial checklist. For every new rule, give an example, explain what it is for and say when an exception makes sense. Keep style preferences apart from performance hypotheses. Never turn a single post, good or bad, into a permanent rule. Return a short change log for my approval and suggest one controlled experiment for next month.

**In ChatGPT:** once you approve the change log, apply it in three places: replace the voice guide source in the project, edit the project instructions and update the skill if you created one. If the rubric from prompt 8 lives in a skill, change it in the same pass so reviews and drafts follow the same rules. With reference chat history switched on, ChatGPT can draw on earlier chats and shows which past chat it used, so you can ask it to collect last month's edits for you. Keep a dated copy of each version of the guide as a separate source, so a later chat can compare this month's rules with last month's.

**Why it matters:** this is how the workflow gets easier to run over time. Your corrections become usable guidance, and ideas about performance stay testable instead of hardening into superstition.

## How do you roll out all 12 prompts?

Add the workflow in stages, so you can check each output before taking on another recurring job.

**Week 1, foundation:** load your business context, run prompt 1 and test the voice guide against writing you would really publish. Do all of it inside the LinkedIn project so the guide ends up as a source.

**Week 2, research backlog:** run prompts 2-4 on a manageable set of customer conversations, market sources and LinkedIn examples. Deep research handles prompt 3, while prompts 2 and 4 work best with files attached.

**Week 3, first batch:** use prompts 5-7 to turn approved material into a calendar and a few drafts. Let Work produce the calendar file and keep the drafting in Chat.

**Week 4, review:** run prompt 8, publish the posts you approve, give each one its first hour of attention and write down the changes you make. The review runs in its own chat.

**Weeks 5-6, conversations:** add prompts 9 and 10 where you have enough sales context and inbound activity for them to be useful. Now is the time to check which of your apps, such as the CRM, appear under Settings > Plugins.

**Every month after that:** use prompts 11 and 12 to review results, improve the instructions and pick the next experiment. Prompt 11 is the first one worth scheduling.

For every recurring job, decide its inputs, destination, frequency and owner. Test a run before you rely on the schedule, and ask ChatGPT to report missing access or unfinished work.

ChatGPT offers three ways to repeat a job. Work can run on a schedule, which suits prompt 3 every Monday or prompt 11 on the first of each month. Simpler scheduled prompts are set up in chat and managed in Pulse, where you can see and edit them all. On eligible accounts, a Work run can also be triggered by an event in a connected app, such as a new message in the Slack channel where your team posts call notes, which keeps prompt 2 supplied. If a scheduled run uses local files from the desktop app, the app has to be open and the computer awake.

A scheduled Work request for prompt 3 could say: "Every Monday at 8 AM [time zone], run the X research prompt in this project with the sites from last week's plan, deliver the report as a document and list any source you could not open." Run it once by hand in the same project first and compare the result with what you would have chosen yourself.

You do not need 12 separate automations on day one. Start with the job that takes the most time and produces something you can judge reliably.

## What can't ChatGPT do for you?

ChatGPT can organise research, find useful evidence, prepare drafts and learn from your edits. The quality still depends on the experience and judgment you feed into the system. Projects, Work and plugins organise the inputs; the argument remains yours.

That means holding a real opinion, being willing to explain it and noticing when a polished draft says something you would never defend face to face.

## What should you do first?

Open a LinkedIn project in ChatGPT, load your context and run prompt 1, because better inputs improve every step that follows. When the first posts go live and you want people reading them in the first hour, [sign up for LinkedInLikes](https://linkedinlikes.com/signup) and add the post link.

## More articles

- [Build an AI content system for LinkedIn from one empty folder (full 2026 setup guide)](https://linkedinlikes.com/blog/how-to-build-an-ai-content-system-for-linkedin)
- [LinkedIn maxxing with Claude](https://linkedinlikes.com/blog/linkedin-maxxing-with-claude)
- [The 2027 LinkedIn marketing playbook](https://linkedinlikes.com/blog/the-2027-linkedin-marketing-playbook)

## Frequently asked questions

### What is LinkedIn maxxing with ChatGPT?

Using ChatGPT for the groundwork behind your LinkedIn posts: a voice guide, customer research, trend research, drafts, editorial review and reporting. You keep the argument and the final decision on what gets published.

### Can ChatGPT publish to LinkedIn for me?

This workflow keeps publishing, comments, connection requests and messages with you. ChatGPT drafts and organises, and you post. Set connected apps to Always ask or Allow read actions so nothing changes without your approval.

### Where should I store my LinkedIn context in ChatGPT?

In a dedicated project. Put the rules in the project instructions, which override your general custom instructions inside the project, and the material itself in the project's Sources.

### Should I use Chat or Work for LinkedIn content?

Chat for drafting, because you want a conversation about the argument. Work for files: the content calendar, spreadsheets of themes and monthly reports.

### Should I build a Custom GPT for my LinkedIn workflow?

No. Custom GPTs are being retired on December 11, 2026. Turn a stable process into a skill under Plugins > Skills instead.

### How many files can I attach to one ChatGPT message?

Up to 20, so you can send your best posts and a few transcripts together when you build the voice guide.