If you give AI a topic and ask it to write a LinkedIn post that leaves out the work that makes the post worth reading: understanding your buyers, finding an interesting angle, pulling together evidence, and knowing what you can credibly say.
These 12 prompts turn that work into a repeatable system, from researching your market to building a content calendar and identifying which posts contribute to real business conversations.
Here's what you'll build:
- A voice guide based on how you actually communicate.
- A research process that finds useful ideas before you need them.
- A draft and review workflow with specific quality standards.
- A reporting loop that helps you decide what to write next.
ChatGPT suits this because one project can hold your instructions, files and linked Drive folders, and Work can turn a finished brief into a document or spreadsheet you can edit.
Save this for your next content planning session.
## Before You Run a Single Prompt: Load Your Business Context
ChatGPT needs more than your job title and a request to "sound human." Give it enough context to understand who you sell to, what you believe, and which claims you can support.
Paste this first:
> Use the following as the working context for my LinkedIn content. Format it as a reference document I can save as a source in this project, and tell me which parts belong in the project instructions instead.
> Business: [Company name, website, product or service, commercial model, and typical sales cycle].
> Audience: [Buyer roles, company sizes, industries, and locations]. Their main problems are [problems], their common objections are [objections], and the events that make them look for help are [triggers].
> Positioning: We help [audience] achieve [outcome] through [approach]. Our strongest differences are [differences]. We are not a good fit for [disqualifiers].
> Viewpoint: I believe [three opinions about my industry]. I disagree with [common advice] because [reason].
> Evidence: Use these approved case studies, results, examples, and personal experiences: [sources]. Do not invent customers, revenue figures, stories, or quotations.
> Voice: Here are [10-20 posts] and [transcripts or writing samples] that sound like me. Avoid [phrases, tones, and formatting habits].
> Goals: My primary LinkedIn objective is [qualified conversations / deal support / recruiting / awareness]. My offer is [offer], and the next step I want interested readers to take is [action].
> Working rules: Use connected sources only where access is available and authorized. If a source is inaccessible, ask for an export or pasted material. Keep LinkedIn publishing, comments, connection requests, and messages with me. Draft into [review destination], distinguish evidence from inference, and flag anything that needs my input.
Keep that context current as your offer, audience, and opinions change.
### Where ChatGPT keeps this context
Open the left sidebar, choose New project, and call it something specific, such as "LinkedIn content". A project groups chats, files and instructions, and every new chat inside it starts from the same background. Paste the rules from the prompt above into the project instructions: inside the project they take priority over your general custom instructions. A short version could read: "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 if a post has already been published."
Put the material itself into the project's Sources. You can upload files, paste a link to a Google Drive file or folder, paste a Slack channel link, add a quick text note, or save a good ChatGPT answer straight into the project so it becomes reusable knowledge. In the desktop app a project can also be attached to a folder on your computer, which suits a writer who keeps drafts as local files.
Pick the memory setting on purpose. Default memory lets the project draw on everything ChatGPT remembers about you. Project-only memory keeps it sealed, which is cleaner for client work, but ChatGPT Work is not available in project-only projects, and a shared project always uses project-only memory. If you plan to use Work for calendars and reports, keep default memory.
Two more places matter. Settings > Personalization holds custom instructions and memory, so put rules that apply to every chat there, such as phrases you never use. Once the process is stable, turn it into a skill (Plugins > Skills, with the built-in skill-creator where your account has it). Custom GPTs are being retired on December 11, 2026, so don't start a new one for this.
Finally, back the "keep publishing with me" rule with a setting. Under Settings > Plugins, each 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 ask before anything that changes data.
## PART 1: RESEARCH + CONTENT PRODUCTION
Prompts 1-8
## 1. Build Your Voice Guide
A useful voice guide should explain the choices behind your writing, including how you introduce an argument, use examples, and move between ideas.
"Conversational and authoritative" won't get you very far.
> Analyze my supplied posts and transcripts to build a practical LinkedIn voice guide.
> Identify:
> How I open posts, explain ideas, and close an argument.
> My usual vocabulary, sentence lengths, paragraph lengths, and use of lists.
> How I express disagreement, uncertainty, humor, and confidence.
> Phrases I use naturally, with verbatim examples and source references.
> Writing habits that appear in weaker examples but should not become rules.
> Create a list of phrases and patterns to avoid, including unnecessary repetition, manufactured drama, unsupported certainty, and excessive one-line paragraphs.
> Finish with three before-and-after examples showing how you would edit generic copy into my voice without changing its meaning. Keep the guide specific enough that another writer could apply it.
**In ChatGPT:** run this inside the LinkedIn project. You can attach up to 20 files in one message, so export your best posts and two or three transcripts and send them together instead of pasting one at a time. Include a couple of weaker posts on purpose and label them, so the guide can separate habits worth keeping from ones to drop. When the guide is right, save that answer into the project as a source, from then on every chat in the project can cite it. If you edit the guide later, edit it in the chat's writing block: highlight the section and ask for a change to that part only. Afterwards, ask "What do you remember about me?" and remove any old memory that contradicts the new guide, so it doesn't leak back into drafts.
**Why this matters:** A good voice guide makes feedback reusable. Instead of correcting the same phrasing every week, you can improve the instructions that shape every draft.
## 2. Find the Buyer Questions Worth Answering
Your sales calls and customer conversations can reveal questions that never show up in a keyword tool. The useful ones usually involve a decision someone is struggling to make.
> Review the supplied sales calls, support conversations, customer interviews, and closed-lost notes from [period].
> Group recurring questions into:
> Problems buyers are trying to understand.
> Concerns that delay a decision.
> Alternatives they are comparing.
> Expectations that lead to disappointment.
> Questions they ask right before they commit.
> For each theme, include an anonymized quotation, source reference, number of distinct conversations where it appears, buyer role, and stage in their decision.
> Suggest two LinkedIn angles per theme: one that helps the reader diagnose the problem and one that helps them evaluate a solution. Rank the top 10 by recurrence, commercial relevance, and the strength of our available evidence.
> Separate explicit buyer statements from your interpretation.
**In ChatGPT:** if the call notes live in Google Drive or Slack, connect those apps under Settings > Plugins and call them in the chat with @ (or pick them from the + menu). ChatGPT may ask before it reads, review the request and approve it. For a big archive, switch the message box from Chat to Work and ask for a spreadsheet of themes with one row per quotation. If the notes name customers, ask ChatGPT to swap names for roles before it builds the table. Work returns a file you can sort and check, which is easier to audit than a long chat answer. Deep research can also treat connected apps as trusted sources, which helps when the notes are split between Drive and Slack.
**Why this matters:** This gives you a content backlog tied to conversations your business is already having, with language you can recognize and evidence you can check.
## 3. Find Topics Moving From X Into Your Market
A discussion gaining attention on X can become a useful LinkedIn post, provided you explain why it matters to your buyers and contribute something beyond a rewrite.
> Using web search, deep research focused on the sites I list, and the X posts and threads I link or paste, review the last [seven days] of discussion around [topics] and these accounts: [accounts].
> Find up to 10 developments or arguments relevant to [buyer].
> For each, return:
> The original source, author, publication date, and link.
> A neutral summary of what happened or what is being argued.
> The strongest competing interpretations.
> The practical implication for our audience.
> A possible angle supported by our experience or evidence.
> Label engagement figures as a snapshot, avoid treating popularity as proof, and check factual claims against original sources where possible.
> Recommend three ideas worth developing, explaining what I would need to contribute before each becomes a credible LinkedIn post.
**In ChatGPT:** ChatGPT searches the web, but none of OpenAI's documentation we checked describes a native X search, so treat X as one more website. Start deep research from the + menu or the sidebar, edit the research plan before it runs, and restrict it to the domains you trust, in the plan, add a line such as "skip sites that only repost the original". It shows progress, lets you change direction mid-run, and delivers a cited report you can download as Markdown, Word or PDF. For posts a search won't surface, paste the links yourself. In the desktop app, the built-in browser can open the pages you send it to. Connected-app information can also shape the queries ChatGPT runs when it searches the web, so the links your team shares in a connected Slack channel give the search better starting terms.
**Why this matters:** Research gives you a starting place, and your judgment gives readers a reason to follow you. A useful adaptation connects the discussion to a decision they actually face.
## 4. Analyze Posts That Outperform Their Authors' Usual Results
Comparing every post with a celebrity account's biggest hit will distort your research. Compare a post with that author's typical results first, then examine what might explain the difference.
> Analyze this supplied LinkedIn swipe file and the available performance data: [file].
> Where enough comparable data exists, compare each post's engagement with the author's median for a similar format and period. Show the calculation and flag small samples, missing metrics, and unusually recent posts.
> For the strongest outliers, identify:
> The topic and intended reader.
> The opening, argument, supporting evidence, and closing.
> The format and approximate length.
> Whether the discussion suggests genuine buyer relevance.
> Possible confounding factors, including a launch, news event, or unusually large distribution.
> Produce five reusable structural patterns, then propose an original idea for each using my own expertise. Do not copy distinctive wording or present an observed pattern as an algorithm rule.
**In ChatGPT:** attach the swipe file as a CSV or spreadsheet rather than screenshots, so ChatGPT can compute the medians and show its working in a table you can re-check. Say in the message which columns hold reactions, comments and reposts, so no median is calculated on the wrong field. Ask for the calculation as a downloadable sheet if you want to reuse it next month, and keep the raw export in the project Sources so the next run starts from the same baseline. For a quick sanity check on one post, our [LinkedIn engagement rate calculator](/tools/linkedin/engagement-rate-calculator) gives you a number to compare with the author's median.
**Why this matters:** You get a more useful set of hypotheses to test, while avoiding the assumption that copying a successful post's layout will reproduce its results.
## 5. Turn Recorded Expertise Into Original Posts
A transcript can contain a strong post buried inside a story, an offhand explanation, or a disagreement that deserves more attention than it received in the original conversation.
> Review this interview, podcast, webinar, or meeting transcript: [source].
> Find eight moments that could support an original LinkedIn post. Prioritize decisions, mistakes, surprising observations, practical explanations, and opinions backed by experience.
> For each moment, provide:
> The exact quotation and timestamp or source location.
> The main idea and who would benefit from it.
> Any context needed to avoid misrepresenting the speaker.
> Supporting facts already available.
> One follow-up question that would make the idea stronger.
> Select the three strongest moments and draft a post from each using my voice guide. Preserve the speaker's level of certainty, remove confidential details, and leave placeholders wherever a result or example needs confirmation.
**In ChatGPT:** upload the transcript file or mention Google Drive with @ and name the file. Because the voice guide is already a source in the project, you don't need to paste it again, just 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 as they appear in the file so you can jump to the moment in the recording. Reviewing the quotations before any writing starts makes it easier to catch a misread timestamp. Client recordings deserve a quick check for names before upload: on Business, Enterprise and Edu plans your content isn't used for training by default, while on personal plans that depends on your "Improve the model for everyone" setting.
**Why this matters:** You can produce content from expertise that already exists without asking someone to manufacture a fresh opinion every time the calendar needs filling.
## 6. Build a Four-Week Content Calendar
A calendar should balance what buyers need to understand with what you have enough evidence to say. Filling every slot with a different version of your offer makes the feed repetitive.
> Build a four-week LinkedIn calendar at [three posts per week] using the approved ideas from our research.
> Include a mix of:
> Practical explanations that help buyers solve a problem.
> Opinions supported by experience or evidence.
> Customer examples we have permission to share.
> Answers to objections and decision-stage questions.
> Occasional offer posts with a relevant next step.
> For each post, specify the reader, single objective, central argument, source material, suggested format, and intended reader action.
> 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 through four.
> Leave room for one timely replacement each week rather than assuming the calendar must remain 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. Ask for a column with the source file behind each post, so every brief stays linked 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 fix the posting slots, check [the best time to post on LinkedIn](/tools/linkedin/best-time-to-post) for your audience, and ask Work to mark which slots are fixed and which one each week is the open replacement. When a brief calls for a carousel, outline the slides in [our carousel maker](/tools/linkedin/carousel-maker). When the plan changes mid-month, ask for a revised version in the same Work chat rather than a new one, so the earlier briefs stay in context.
**Why this matters:** A useful calendar reduces weekly planning work while leaving room for better ideas, new evidence, and changes in the market.
## 7. Draft Posts With Something to Say
The research should determine the argument before ChatGPT starts polishing sentences. Otherwise, you can end up with clean writing that takes several paragraphs to state the obvious.
> Write three LinkedIn drafts from these approved briefs: [briefs].
> Use my business context and voice guide. For each draft:
> Make one clear argument for one intended reader.
> Open with a specific observation, tension, result, or question supported by the brief.
> Explain the reasoning with a concrete example.
> Use bullets where they make parallel items easier to scan.
> Vary sentence and paragraph length, allowing occasional one-line emphasis.
> End with a useful implication or natural next step.
> Aim for [180-300 words] unless the idea warrants a different length. Avoid repeated conclusions, invented personal stories, exaggerated promises, and formulaic contrasts.
> Supply two alternative openings and explain briefly how they change the emphasis. Flag missing evidence separately from the publishable copy.
**In ChatGPT:** stay in Chat for this one, not Work: you want a conversation about the argument, not a finished file. Each draft lands in a writing block, so you can select one paragraph and ask for a fix without regenerating the rest. Ask for each draft in its own 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](/tools/linkedin/hook-generator) and paste the best line back in for ChatGPT to build on. The project instructions already carry your rules, so the drafting message can stay short: the briefs, plus "use the voice guide source".
**Why this matters:** Clear constraints give you drafts that are easier to judge. Your review can focus on the quality of the argument instead of repairing unnecessary filler.
## 8. Create a Separate Editorial Review
Give the reviewer a defined job: find weaknesses in the draft and explain how to fix them. A reassuring "This is strong" is not an editorial process.
> Review these LinkedIn drafts against the brief, source material, business context, and voice guide.
> Score each out of 100 using:
> Buyer relevance: up to 25.
> Specificity and supporting 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 the evidence behind each score. Check every factual claim, identify generic passages, and highlight repetitions, abrupt one-line fragments, and paragraphs that would work better as lists.
> Return a publishable revision, a short explanation of meaningful changes, and any unresolved questions. Treat invented evidence, exposed confidential information, or a misrepresented source as a blocking issue regardless of score.
> Keep everything in the review document for human approval.
**In ChatGPT:** open a new chat in the same project for the review. The reviewer then sees the brief, sources and voice guide, but not the back-and-forth that produced the draft, so it is less likely to defend its own choices. If you run this rubric every week, save it as a skill so the scoring never drifts, eligible accounts can start one by typing @skill-creator and pasting the rubric. Before you publish, paste the revision into our [LinkedIn post analyzer](/tools/linkedin/post-analyzer) and check the length with the [character counter](/tools/linkedin/character-counter). Keep the scored version in the review chat, so prompt 12 has the before and after side by side.
**Why this matters:** A review rubric makes your standards explicit. The score helps organize feedback, while the source checks and editor's judgment determine whether the post is ready.
## The first hour after you publish
The other MAXXING techniques all come back to the same thing: in the first, golden hour after the post goes live, get as many reactions on it as you can.
**Start with people you can reach.** Colleagues, friends, relatives, clients: anyone you can actually get in touch with. Ask them for a reaction while the post is fresh.
**No one to ask? Use a specialized service, but only one built on quality engagement, with moderation.** Below is the list of groups we have already checked for our other articles. They are a good place to start.
**aaas.market is our service.** It runs on real people. Moderation is held to the highest level and the audience is the highest quality we know of, because that is where most of our attention goes. It is a closed community: you get in after a verification. It supports every type of engagement.
**Connect ChatGPT through MCP.** An agent like ChatGPT needs MCP to get engagement. aaas.market has an MCP server at and through it ChatGPT can request any type of engagement on a post you have already published. To add it in ChatGPT on the web:
1. Open [Plugins](https://chatgpt.com/plugins), select the plus button, then Add custom MCP server.
2. Name it aaas.market. Under Connection, enter the server URL
3. For authentication, choose OAuth. Review the risk warning and tick "I understand and want to continue".
4. Select Create as a plugin, check the tools it discovered, and install the plugin. The first time you connect, an aaas.market sign-in page opens, sign in there and approve the access.
5. In a new chat, type @, pick aaas.market, and give it the link to your live post.
Custom servers are added on the web, not in the mobile app. Account and workspace policies apply, and ChatGPT asks you to confirm write actions by default. Once installed, the plugin also appears in the + menu under the message box. If ChatGPT doesn't reach for it on its own, name it in the request, for example "Use the aaas.market app for this." To see exactly what was sent, expand the tool call in the chat: the full input and output are shown. Publishing, comments, connection requests and messages stay with you.
Start free on aaas.market
## LinkedIn engagement groups you can join
Free communities we checked on Oct 21, 2026. Member counts are what each platform showed that day. Each group is run by its own admins, so check the pinned message before your first post.
| Group | Platform | Members | Language | Best for |
|---|---|---|---|---|
| [LinkedIn Engagement Pod & Group](https://t.me/linkedin_engagement_pod) | Telegram | 1,833 members | en | general LinkedIn engagement pod (likes + comments) |
| [LinkedIn Engagement Pod (RVision Digital)](https://t.me/linkedinengagementpod) | Telegram | 1,064 members | en | share posts for likes/comments, connections, skill endorsements |
| [Linkedin engagement pod](https://t.me/linkedinpod1) | Telegram | 488 members | en | general LinkedIn engagement pod |
| [ Social Linkedin/FB/IG Engagement Pod](https://t.me/socialengagementpod) | Telegram | 77 members | en | LinkedIn pod: react + meaningful comment on last 5 posts before shari... |
| [LinkedIn For Breakfast](https://www.skool.com/linkedin-for-breakfast) | Skool | 6,603 members | en | free LinkedIn community with engagement pods, live events |
| [Linkedin Likes and Engagement](https://t.me/linkedinlikesandengagement) | Telegram | 697 members | en | likes/engagement exchange + connections |
| [Linkedin Pod (SF founders like-for-like)](https://chat.whatsapp.com/DBOyIPrDR5IK27yzW606sF) | WhatsApp | n/a (WhatsApp hides count) | en | founders like-for-like on LinkedIn |
| [r/linkedintips](https://www.reddit.com/r/linkedintips/) | Reddit | 38,314 members | en | LinkedIn tips for promoting your brand |
| [LinkedIn Club](https://t.me/linkedinclub) | Telegram | 552 members | en | LinkedIn networking/connections community |
| [r/LinkedInGrowth](https://www.reddit.com/r/LinkedInGrowth/) | Reddit | - | en | LinkedIn growth and connections |
Want real engagement on your own posts without searching through groups? Start free on aaas.market.
## PART 2: PIPELINE + PERFORMANCE
Prompts 9-12
## 9. Build Content Around Stalled Deals
Some of your most useful content may answer a concern that several prospects have raised privately. The goal is to explain the issue well enough that future buyers arrive better informed.
> Review the supplied CRM records and call notes for qualified deals that stalled during [period].
> Identify recurring objections and distinguish:
> Explicit objections recorded in conversations.
> Possible explanations that remain unconfirmed.
> Operational delays that content is unlikely to solve.
> For each repeated objection, record the buyer role, deal stage, available evidence, and the question a useful LinkedIn post should answer.
> Recommend five content briefs. Each should explain the concern fairly, show where our approach is appropriate, acknowledge where it is not, and identify the proof required.
> Draft the two strongest posts without naming prospects or revealing deal details. Separately, suggest which approved existing resources a salesperson could share in a relevant conversation.
**In ChatGPT:** check whether your CRM is available as an app under Settings > Plugins, availability depends on your plan, region and workspace, and an admin may have to enable it. If it isn't there, export the stalled deals to a spreadsheet and attach it. Set the app permission to Always ask or Allow read actions for this job, since nothing here should change a record. On Business, Enterprise and Edu plans, information from apps is not used for training by default. Ask ChatGPT to cite the record or note behind every objection, so you can open the source before a brief goes into the calendar.
**Why this matters:** This connects marketing with the questions salespeople repeatedly need to answer. It also helps you avoid writing persuasive content about problems your buyers do not consider important.
## 10. Prepare Relevant Conversation Briefs
When someone responds to a post, the best next step depends on what they said and whether a business conversation would be welcome. Engagement alone does not show real business interest.
> Review these LinkedIn conversations I have supplied: [comments, inbound messages, or manually recorded interactions].
> Group them into:
> Questions that deserve a useful public answer.
> Conversations where sharing a resource would help.
> Explicit interest in our offer.
> General discussion with no commercial next step.
> For each relevant conversation, summarize the person's stated need, available business context, and what remains unknown.
> Draft a response that addresses their actual message before introducing anything else. Where appropriate, suggest one follow-up question or a resource we already have.
> Do not infer business interest from a like, invent familiarity, or force a meeting request into a conversation that does not support one. Keep all replies as drafts for me to review and send.
**In ChatGPT:** paste the comments or a copy of the message thread, ChatGPT works from what you give it here. Ask for the four groups as a table with one row per person, and keep this in its own chat in the project so personal details don't spread into drafting chats. If a thread holds details you don't want kept at all, use a temporary chat, which isn't saved to history or memory. Copy the finished drafts back into the project only after you have removed anything personal. For a public reply that needs a different tone, try the [LinkedIn comment generator](/tools/linkedin/comment-generator) and compare it with ChatGPT's version before you write your own.
**Why this matters:** Preparation can make your responses more thoughtful without turning every interaction into a sales sequence. Sometimes the right outcome is simply a useful answer.
## 11. Report on Business Results With Clear Attribution
Impressions help you understand distribution, but they cannot tell you whether the right people became interested or whether a post contributed to a deal.
> Combine my supplied LinkedIn analytics, tracked website visits, inbound inquiry records, and relevant CRM data for [period].
> Report on:
> Reach and engagement by post, topic, and format.
> Qualified inbound conversations.
> Resource sign-ups and demo requests where tracking exists.
> Opportunities with documented LinkedIn touchpoints.
> Content production time and review effort.
> Separate directly tracked conversions, self-reported discovery, and possible influence. Do not attribute an entire deal to LinkedIn because one contact engaged with a post.
> Highlight posts that attracted relevant buyers, posts that reached a broad but less relevant audience, and posts with insufficient data to judge.
> Finish with three findings, three limitations, and one recommended experiment. If possible, assess posts at consistent ages so newly published content is not unfairly compared with older posts.
**In ChatGPT:** hand 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 it to keep every figure traceable to a row in the file, and to open the report with a short methods note: which exports, which date range, and which posts were left out and why. To estimate reach for posts where the export is incomplete, our [LinkedIn impressions calculator](/tools/linkedin/impressions-calculator) gives you a consistent number to log. If part of the evidence sits on public sites, such as a competitor's published case study, deep research is the better tool, Work is the better one when every input is your own file.
**Why this matters:** Better reporting gives you a reason to repeat or change an approach. It also makes uncertainty visible instead of turning incomplete tracking into a confident revenue claim.
## 12. Turn Feedback Into a Better System
Every edited draft contains information about your standards. Capture the recurring lessons so you spend less time correcting familiar problems.
> Compare the original drafts, my edits, final published versions, and available performance data from [period].
> Identify:
> Voice corrections I have made repeatedly.
> Claims that needed more evidence.
> Ideas that became clearer after a change in framing.
> Formatting changes that improved readability.
> Topics that generated useful buyer conversations.
> Propose updates to the voice guide, research briefs, and editorial checklist. For every proposed rule, show an example, explain its purpose, and note when an exception may be appropriate.
> Keep stylistic preferences separate from performance hypotheses. Do not create a permanent rule from one successful or unsuccessful post.
> Return a short change log for my approval and suggest one controlled experiment for the next month.
**In ChatGPT:** when 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 made one. If the prompt 8 rubric lives in a skill, change it in the same pass so reviews and drafts follow the same rules. With reference chat history turned on, ChatGPT can pull details from earlier chats and shows which past chat it used, so you can ask it to collect last month's edits instead of pasting them. Keep a dated copy of each guide version as its own source, so a later chat can compare this month's rules with last month's.
**Why this matters:** This is how the workflow becomes easier to manage over time. Your corrections become usable guidance, while performance ideas remain testable rather than hardening into superstition.
## How to Use All 12 Prompts
Build the workflow in stages so you can inspect the output before adding another recurring job.
Week 1: Establish the foundation. Load your business context, run prompt 1, and check the voice guide against writing you would actually publish. In ChatGPT, do all of this inside the LinkedIn project so the guide ends up as a source.
Week 2: Build your research backlog. Run prompts 2-4 using a manageable set of customer conversations, market sources, and supplied LinkedIn examples. Deep research handles prompt 3, prompts 2 and 4 work best with files attached.
Week 3: Produce the first batch. Use prompts 5-7 to turn approved material into a calendar and a small number of drafts. Let Work produce the calendar file, and keep drafting in Chat.
Week 4: Strengthen the review process. Run prompt 8, publish the posts you approve, run the first hour on each post you publish, and record the changes you make. The review runs in its own chat, and aaas.market is connected as a plugin by now.
Weeks 5-6: Connect content with conversations. Add prompts 9 and 10 where you have enough sales context and inbound activity to make them useful. This is the moment to check which of your apps, such as the CRM, are available under Settings > Plugins.
Then repeat monthly: Use prompts 11 and 12 to review results, improve the instructions, and choose the next experiment. Prompt 11 is the first one worth putting on a schedule.
For any recurring job, define its inputs, destination, frequency, and owner. Test a run before relying on the schedule, and have ChatGPT report missing access or incomplete work.
ChatGPT gives you three ways to repeat a job. Work can run on a schedule, which fits prompt 3 every Monday or prompt 11 on the first of the month. Simpler scheduled prompts are created in chat and managed in Pulse, where you can see and edit them all. On eligible accounts, a Work run can also start from an event in a connected app, such as a new message in a Slack channel where your team drops call notes, which keeps prompt 2 fed. 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 might read: "Every Monday at 8 AM [time zone], run the X research prompt in this project with the sites from last week's plan, produce 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 output with what you would have picked yourself.
You do not need 12 separate automations on day one.
Start with the job that consumes the most time and produces an output you can reliably evaluate.
## The Real Talk
ChatGPT can help you organize research, retrieve useful evidence, prepare drafts, and learn from your edits. The quality still depends on the experience and judgment you bring into the system. Projects, Work and plugins organize the inputs, the argument is still yours.
That means having a real opinion, being willing to explain it, and noticing when a polished draft says something you would never defend in a conversation.
If you want the first engagement on the posts this workflow produces, start free on aaas.market.
Otherwise, open your LinkedIn project, load your context and run prompt 1. Improving the inputs will make every step after it more useful.