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.
A dot is a good fit for this kind of system: it is an always-on agent in ChatGPT with its own cloud computer and browser, it keeps working between conversations, and it comes back to you when a decision needs your judgment.
Save this for your next content planning session.
## Before You Run a Single Prompt: Load Your Business Context
ChatGPT Dots 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. Save the essentials to your notes, keep the full version as a reference I can reuse, and tell me exactly what you saved and where.
> 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 Dots keeps this context
Your dot works from three layers: the conversation you are in, the relevant parts of your ChatGPT memory, and its own saved notes about your preferences, decisions and ongoing work. Those notes follow it across ChatGPT, Slack, Teams and calls, so you can brief it once at your desk and correct it later from your phone. They are not a transcript, though, and they are separate from ChatGPT's saved memory: switching a memory setting doesn't necessarily change what the dot already wrote down. When the context changes, tell the dot directly and ask it to confirm the update. Once a week, ask "What are you keeping in your notes about my LinkedIn work?" because those notes are what it brings into every new conversation and channel.
Two settings turn the "working rules" part of the prompt into something firmer. Instructions describe how you want it to work, for example "show me every draft before it goes anywhere". A fuller version: "For LinkedIn work, use my business context and voice guide from your notes. Send drafts and questions to me in ChatGPT, keep routine progress to a weekly summary, and tell me when a source you need is not connected." Custom Rules (Settings > Personalization > Custom rules) attach one of four behaviours to an action: take action without asking, take action when you say so, ask before taking action, or hand off to you. Write one rule for anything that would appear on LinkedIn under your name and set it to Hand off to you. Neither instructions nor rules switch off the built-in safeguards: Auto-review still checks any action that could affect your accounts or share information before the dot goes ahead.
One detail saves confusion later. When your dot delegates a job to a background agent or a new cloud thread, that job gets its own conversation and does not automatically see everything you told the dot. Ask the dot to pass the business context and the voice guide along with every job it hands off.
## 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 Dots:** share the posts and transcripts in the dot's conversation, or keep them in a Google Drive folder and let the dot read them through the Drive plugin. OpenAI's own content-creator example is close to this job: the dot learns your voice and audience, drafts for your approval, and carries your edits into the next batch. That only works if the guide is somewhere it can find again, so ask it to keep the guide on a Page in Space and to note in its own notes where the Page lives. Review that first guide and correct what is missing before you widen the job, which is how OpenAI suggests starting any new responsibility. Connect only the plugins this job needs, adding Slack or Teams as a messaging channel doesn't give the dot access to your apps or files.
**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 Dots:** this is where a dot earns its keep, because the questions keep arriving after you run the prompt once. If call notes land in Gmail or a Slack channel, connect those plugins and tell the dot which channel or label to watch, connecting Slack on its own doesn't start any monitoring. Its proactive research can read what it has permission to read and keep private notes, but it never sends or changes anything in that mode. Ask it to add new quotations to the backlog and to message you only when a theme moves into the top 10. In the desktop app, Activity lists each research job it started, so you can open one and see which sources it read.
**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 your own browser, web search, and the X links I share, 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 Dots:** the dot researches in its own cloud browser, which runs on its own sessions, separate from the browser on your computer. OpenAI doesn't document a direct X connection, and some websites block cloud browsers, if X pages don't load, ask the dot to try your connected computer instead, or paste the links. Ask it to name the source domain for every item, so you can see where each story came from, and to drop anything older than the window you set. Run the prompt once now to check the format, then make it recurring with a full schedule: "Every Monday at 8 AM [time zone] for the next eight weeks. Message me only if a story fits [buyer]. Confirm the schedule." Say which changes deserve a message and which can wait for a weekly summary, or you will hear about everything.
**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 Dots:** the dot analyzes data on its own computer, so give it the swipe file as a spreadsheet and ask it to return the medians as a file you can open. A large file is a good case for background agents: the dot can split authors between them and keep talking to you while they run. Before it starts, tell it which fields in the file count as engagement, so every background agent calculates the same thing. To benchmark one of your own posts against the author medians it finds, use our [engagement rate calculator](/tools/linkedin/engagement-rate-calculator). Ask the dot to save the median table to the voice guide Page, so prompt 12 can compare next month against the same numbers.
**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 Dots:** OpenAI lists "prepare content from each interview" as a dot use case, so give it the standing job, not a one-off: whenever a new transcript appears in the Drive folder you name, run this prompt and send you the eight moments, plus a short list of the moments it rejected and why, so you can overrule it. You can review the quotations and adjust the tone of a draft from your phone, then publish yourself. Ask the dot to keep each speaker's original certainty in its notes so the next batch doesn't drift. If the recording sits only on your laptop, connect your computer through the dot's profile, it has to be online with the ChatGPT app open while the dot works with it.
**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 Dots:** treat the calendar as a responsibility the dot owns, not a file it hands over once, and ask it for one line per post naming the source file behind it. Ask it to keep the calendar on a shared Page, swap in the timely replacement when prompt 3 finds something better, and flag a slot that has lost its evidence. Changes to the plan then show up on the Page instead of in a new file each week. Pick the posting days with [the best time to post on LinkedIn](/tools/linkedin/best-time-to-post). The dot keeps the plan current, for a nudge when each post is due, our [free social media scheduler](/tools/free-tools/free-social-media-scheduler) reminds you by email or Telegram, and you publish. Tell the dot which calendar changes need you, such as a slot with no approved source, and that routine reshuffles can wait.
**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 your dot 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 Dots:** tell the dot where drafts should land, for example a private Slack channel for drafts and ChatGPT for questions, so routine output doesn't bury decisions. Away from your desk, you can approve or send back a draft from the mobile app, it is the same dot on every device. Your Hand off to you rule keeps the last step with you. When an opening feels flat, generate a few more in our [hook generator](/tools/linkedin/hook-generator), and if a draft needs a stronger structure for reach, run it through the [LinkedIn post booster](/tools/linkedin/post-booster) before you send it back to the dot with your notes. Ask it to label each draft with the brief it came from, so every claim can be traced before you publish.
**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 Dots:** ask the dot to hand the review to a separate cloud thread instead of grading its own writing in the same conversation. The thread appears as its own conversation on desktop, web and mobile, so you can read the scores there, and in the desktop app you can follow it under Activity in the dot's profile, where each job shows its files, results and any request for your input. Before approving, check how the revision looks in the feed with our [post preview](/tools/linkedin/post-preview) and clean up line breaks in the [text formatter](/tools/linkedin/text-formatter). The dot can send follow-up instructions to jobs it created, so once you answer the open questions, ask it to pass them to the reviewing thread for a final revision.
**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
Every other MAXXING technique leads to the same goal. In the first hour after a post goes live, the golden hour, collect as many reactions as you can.
**Ask the people you can reach first.** That means colleagues, friends, family, clients, anyone you can contact directly. Their reactions are the ones worth asking for.
**If there is no one like that, a specialized service can help, as long as the engagement is good quality and moderated.** We have already vetted a set of groups for our other articles, you'll find them in the next section.
**aaas.market is the service we run.** Everyone on it is a real person. We give moderation more attention than anything else, which is why it is held to the highest standard and why the audience is the best we have seen. Entry is through verification, because it is a closed community. Every type of engagement is supported.
**Your dot needs MCP for this step.** Agents like ChatGPT Dots get engagement through MCP, and aaas.market runs an MCP server at Once it is connected, your dot can request any type of engagement for a post you have published. A dot has no separate MCP screen, it uses the plugins installed on your ChatGPT account, so the setup happens in ChatGPT on the web, in a desktop browser:
1. Go to [chatgpt.com/plugins](https://chatgpt.com/plugins), select the plus button and choose Add custom MCP server.
2. Name it aaas.market and paste as the Server URL under Connection.
3. Pick OAuth as the authentication method, read the risk warning, and select "I understand and want to continue".
4. Select Create as a plugin, review the tools, and install it. On the first connection ChatGPT sends you to an aaas.market sign-in, log in and allow access.
5. Ask your dot: "Which apps can you use right now? Is aaas.market one of them?" Then send it the link to your live post.
The plugin can't be added from the mobile app, Slack or Teams, those only carry messages. OpenAI's dots documentation talks about "supported plugins" without listing custom MCP servers by name, so the check in step 5 is how you confirm it on your account. Set the plugin's permission level under Settings > Plugins, Always ask is the strictest. To keep yourself in control, also add a Custom Rule for requesting engagement set to Ask before taking action. If the dot tries another route, such as its cloud browser, tell it to use only the aaas.market connector. To see what it sent, ask it to list the exact tool calls it made, in order. You still publish, comment, connect and message on LinkedIn yourself.
Start free on aaas.market
## LinkedIn engagement groups you can join
These are free communities we checked on Oct 21, 2026, with the member count each platform showed on that date. Admins set their own rules, so read the pinned message before you 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 |
Rather skip the group chats? Start free on aaas.market and get real engagement on your own posts.
## 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 Dots:** connect the CRM as a plugin if one is available for your account, and set that plugin to read-only so the dot can study deals without touching them. If the plugin can write, add a Custom Rule for CRM changes set to Ask before taking action. Deal notes are exactly the private material a dot is careful with: before it shares anything from a private conversation with other people, it checks that you have allowed it. If you run the dot in a team Slack channel, say explicitly that prospect names stay out of the channel. Any drafting job it delegates should get only the anonymized objections, since a new job sees just the context the dot passes to it.
**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 Dots:** forward the comments to the dot wherever you are, including a Slack DM on your phone, and get the briefs back in the same place, one row per person. Because the dot treats a direct request in the conversation as approval, phrase this one as "draft only". Your Hand off to you rule covers anything that would go out on LinkedIn. For a quick public reply in a different tone, compare its draft with one from our [comment generator](/tools/linkedin/comment-generator). You can also talk a tricky reply through on a call with the dot, ending the call doesn't stop work you assigned during it, so the draft arrives afterwards. Ask it to keep a short note of who you have already answered, so the next batch skips them.
**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 Dots:** save this as a monthly schedule with a time zone and an end date, and say where the report goes. Ask the dot to hand the document itself to ChatGPT Work and to tell you which export it was missing. If a source needs a login on a website, the dot sends you a private sign-in request instead of asking for credentials in the chat. A finished run doesn't prove the report is right, so open it and check the sources before you share it. For posts where the analytics export has gaps, log a consistent estimate from our [impressions calculator](/tools/linkedin/impressions-calculator). Ask the dot to open the report with the date range, the exports it used and any posts it left out, so next month's comparison is like for like.
**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 Dots:** a dot is already collecting this material, because it updates its notes as you make decisions and can search your past ChatGPT conversations for earlier ones. After you approve the change log, ask it to update the voice guide Page, rewrite its own notes, drop the notes that no longer apply, and read back what it changed or removed. Then check under Scheduled that the recurring prompts still use the new guide. Stopping a job doesn't undo what it already did, so approve rule changes before the dot applies them to scheduled work.
**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. Set your Custom Rules this week too, before the dot has anything to send.
Week 2: Build your research backlog. Run prompts 2-4 using a manageable set of customer conversations, market sources, and supplied LinkedIn examples. Turn prompt 2 into a standing watch on the channel where call notes arrive.
Week 3: Produce the first batch. Use prompts 5-7 to turn approved material into a calendar and a small number of drafts. The calendar becomes a Page the dot keeps current.
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. Reviews go to a separate cloud thread, and the aaas.market plugin is installed.
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. Forward comments to the dot from wherever you see them.
Then repeat monthly: Use prompts 11 and 12 to review results, improve the instructions, and choose the next experiment. Prompt 11 runs from a saved monthly schedule.
For any recurring job, define its inputs, destination, frequency, and owner. Test a run before relying on the schedule, and have your dot report missing access or incomplete work.
A dot decides on its own when to wake up and follow up, but work that repeats at fixed times needs a saved schedule. Give it four things: what to check or update, when to run (with a time zone and an end date), which changes deserve a message, and where to deliver results. Ask it to confirm what it saved. In the dot's profile, Scheduled shows recurring work and Activity shows delegated jobs and their results. Pause stops the current main job, recurring work is cancelled separately under Scheduled. If you already have ChatGPT Work automations, the dot can read and edit those too.
A saved schedule for prompt 2 could read: "Every weekday at 7 AM [time zone] until [end date], check the [call notes channel] for new conversations, add new quotations to the buyer questions Page, and message me in Slack only if a theme moves into the top 10. Confirm the schedule." When a connected service supports events, you can ask the dot to respond to new messages there instead of checking on a timer, ask it to confirm which events it can follow.
You do not need 12 scheduled jobs on day one.
Start with the job that consumes the most time and produces an output you can reliably evaluate.
## The Real Talk
ChatGPT Dots 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. A dot keeps the work moving between conversations, deciding what is worth saying stays with you.
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 dot, load your context and run prompt 1. Improving the inputs will make every step after it more useful.