Social Media Examiner's newest edition tells marketers to treat Claude and ChatGPT Projects as standing workspaces with written rules, and to build AI workflows that keep running without someone at the keyboard. The tools involved are Anthropic's Claude Projects and OpenAI's ChatGPT Projects, plus industry news about Google Gemini. For marketing teams, this marks a shift from asking an AI for single outputs to designing systems that produce consistent work over weeks.
The roundup from Social Media Examiner is short, but the topics it groups together are worth a closer look. Projects in both Claude and ChatGPT let users attach files, set persistent instructions and keep related conversations in one place. Used well, these features turn a general-purpose chatbot into something closer to a trained assistant that already knows your brand, audience and standards.
- Social Media Examiner published a newsletter edition covering Claude and ChatGPT project tips (Source: Social Media Examiner).
- The edition includes a set of five rules or instructions to give AI projects (Source: Social Media Examiner).
- It also covers building AI workflows that run without the user present (Source: Social Media Examiner).
- The roundup features industry news involving Google Gemini (Source: Social Media Examiner).
Why Do Instructions in Claude and ChatGPT Projects Matter More Than Prompts?
A prompt shapes one response. A project instruction shapes every response inside that workspace. That difference is the main reason the Social Media Examiner edition puts rules first.
When a team member opens a fresh chat and types "write a LinkedIn post about our product launch," the model has to guess at tone, audience, length, banned phrases and formatting. Every person on the team guesses differently, so the output varies too. A project with well-written instructions removes most of that guessing. The brand voice, target persona and approval rules are already loaded before anyone types a word.
This matters even more as AI tools move into shared team spaces. Whizsky has covered how Anthropic's Claude is joining Slack as an AI coworker, and the same rule applies there: an assistant that many people use needs a single source of truth for how it should behave.
Five Instruction Rules Marketers Can Apply to Their Projects
The source teases five rules without listing them in the summary, so the framework below is Whizsky's own. It reflects common practice among teams that rely on project workspaces day to day.
- State the role and the audience. Tell the project who it is working for and who will read the output. "You support a B2B SaaS marketing team writing for operations managers at mid-size companies" is far more useful than "you are a helpful assistant."
- Separate rules from reference material. Put behavioral rules in the instructions field and put brand guidelines, product sheets and past top-performing posts in uploaded files. Mixing them makes both harder for the model to apply.
- Write negative rules explicitly. List the words, claims and formats the brand never uses. Models follow "never promise specific results" more reliably than a vague request to "stay compliant."
- Define the output format. Specify length, structure and deliverable type, such as a hook line, three body lines and a call to action for social posts. Consistent formats make outputs easier to review and reuse.
- Tell it what to do when unsure. Instruct the project to ask a clarifying question or flag missing information instead of filling gaps with assumptions. This one rule prevents a large share of confident but wrong outputs.
| Where it goes | What belongs there | Example |
|---|---|---|
| Project instructions | Persistent rules and behavior | Tone, banned phrases, output format, escalation rules |
| Project files / knowledge | Reference material the model draws from | Brand guide, product specs, audience research, past winning content |
| Individual chat prompt | The specific task at hand | "Draft three Instagram captions for Tuesday's webinar" |
How to Build AI Workflows That Run Without You
The second theme in the edition, AI workflows that run without you, is where things get more ambitious and more risky. A well-instructed project still waits for a human to start each task. An autonomous workflow triggers itself, works through several steps and delivers a result.
Start with a repeatable, low-risk task
Good first candidates are internal and reversible: summarizing weekly social analytics, turning a webinar transcript into draft posts, or sorting inbound messages by intent. Avoid anything customer-facing or anything that spends money until the workflow has a track record. Whizsky's guide to building agentic workflows with Manus for marketing teams walks through how teams scope these first automations.
Write the workflow down before automating it
If a human cannot describe the process as clear numbered steps with defined inputs and outputs, an agent will not run it reliably. Document the trigger, each step, the decision points and what a finished deliverable looks like. That document often becomes the core of the agent's instructions.
Build in checkpoints, not blind trust
"Running without you" should mean running without constant supervision, not without any review. Add a human approval step before anything is published, sent or changed in a live system. Log every output so problems can be traced. As Whizsky reported on WebMCP security risks for AI agents, agents that act on websites and external tools widen the attack surface, so permissions should stay as narrow as the task allows.
What Does the Gemini News Signal for Marketers?
The summary confirms only that the edition includes industry news from Google Gemini, not the details, so Whizsky will not speculate on the specific update. The broader point is plain: Google, Anthropic and OpenAI are all competing on the same ground of persistent context, file-aware workspaces and agentic task execution.
For marketers, that competition argues against locking all workflow knowledge into one vendor's interface. Keep master copies of instructions, brand guides and process documents in your own systems so they can be moved between Claude, ChatGPT and Gemini as capabilities and pricing shift. Teams already experimenting with OpenAI's newer publishing features, covered in Whizsky's piece on how marketers should prep lead magnets for ChatGPT Sites, will find portable documentation saves time when switching tools.
Key Takeaways for Using Claude and ChatGPT Projects
- Treat projects as trained workspaces. Persistent instructions and reference files produce more consistent output than repeated one-off prompts.
- Keep rules and reference separate. Behavior goes in instructions; brand and product knowledge goes in files.
- Automate gradually. Start autonomous workflows on internal, reversible tasks and expand only after they prove reliable.
- Keep a human gate. Approval before publishing and tight permissions protect brand safety as agents gain more autonomy.
- Stay portable. With Gemini, Claude and ChatGPT moving quickly, own your instruction documents so you can switch platforms without starting over.
The newsletter's message is simple: the marketers getting the most from AI are the ones writing better rules for the tools, not the ones writing the cleverest prompts.
Frequently Asked Questions
What are Claude and ChatGPT Projects?
Claude Projects from Anthropic and ChatGPT Projects from OpenAI are workspaces that group related chats together with persistent instructions and uploaded files. They let users give the AI standing context, such as brand guidelines and audience details, so every conversation in the project follows the same rules.
What should marketers put in project instructions?
Project instructions should cover the AI's role, the target audience, output formats, banned words or claims, and what to do when information is missing. Reference material like brand guides and product specs works better as uploaded files than as instruction text.
Can AI workflows really run without human involvement?
AI workflows can run multi-step tasks without constant supervision, but marketers should keep human approval before anything is published or sent to customers. Starting with internal, low-risk tasks and logging every output helps teams build trust in automation safely.
Should marketing teams commit to one AI platform?
Because Google Gemini, Claude and ChatGPT are evolving quickly, teams benefit from keeping their instructions and process documents in their own systems. This makes it easier to move workflows between platforms as features and pricing change.
