# Choose Better Brand Examples for ChatGPT Writing [Read the original article]() By Garry · Founder Published: 2026-09-29T20:07:45.115Z Updated: 2026-09-29T20:13:22Z 6 min read Categories: Content creation Build a small, varied set of writing examples that teaches ChatGPT editorial decisions without copying stale facts or repeated phrases. ![A curated row of distinct mint, ivory and blue ceramic vessels casting different shadows, suggesting a family resemblance without identical forms]() ## Key takeaways - Annotate the principle each example demonstrates and what should not transfer. - Choose representative editorial work rather than only high-engagement posts. - Test on a held-out brief to detect copied patterns and imported facts. Examples teach an assistant what “good” looks like more clearly than a list of adjectives. They can also teach the wrong lesson. If every example begins with a provocative question, ChatGPT may conclude that your brand always needs one. If the examples all announce launches, it may struggle to write a quiet but useful help post. Building a few-shot example set is an editorial selection problem. The aim is to demonstrate principles that should transfer across tasks while making the limits of each example explicit. A small, varied set usually gives you more control than a large archive of unexplained past posts. ## Decide which behavior the examples should teach Choose a narrow purpose before collecting samples. You might want clearer openings, stronger explanations, more natural transitions, or calls to action that fit the reader's stage. “Write like us” is too broad to evaluate without breaking it into observable behavior. A useful example set might show how your team explains one product task, qualifies a limitation, and invites a next step. These are decisions a reviewer can point to in the text. Words such as warm, bold, or human can remain useful shorthand, but they need examples to become operational. OpenAI's [prompt engineering guide]() describes using examples to guide outputs. It does not imply that a model will automatically distinguish a deliberate style choice from an accidental feature of the sample. Your annotation makes that distinction easier. Begin with your [brand voice guidance](), then select examples that demonstrate its most important principles in different situations. ## Choose representative work instead of only popular work A post with high engagement is not necessarily the best writing example. It may have benefited from timing, an unusually interesting topic, or a large distribution push. If you select only top-performing posts, you may accidentally teach a narrow style associated with those circumstances. Include work that your editors would still approve even without knowing its performance. Choose examples with clear facts, useful structure, and a suitable next action. Where performance matters, keep it as separate context rather than treating it as proof that every sentence should be imitated. Use different post jobs: a how-to explanation, a product update, and a response to a common misconception, for example. Make sure the set does not contain only one opening pattern or one sentence length. Diversity should reflect real editorial needs, not random variation for its own sake. Your [content pillars]() can help locate representative tasks. A voice that works only for promotional posts will not serve an educational series well. ## Annotate what to copy and what to ignore For each example, add a short note explaining its purpose. Identify the reader, the useful writing choice, and any detail that should not be generalized. A numerical result may be specific to that campaign. A seasonal reference may be irrelevant next month. An annotation might say: “This example names the problem in the first sentence and explains one corrective action. Preserve that sequence, but do not reuse the opening phrase, product claim, or customer story.” The assistant now has a principle to apply rather than a surface pattern to echo. Use only material you have the right to reuse. If a customer quote appears in an approved post, that does not automatically authorize it in every future context. Remove identifying details when they are not needed to teach the writing behavior. A practical prompt is: > Use these examples to learn our editorial decisions, not to copy their wording. For each, identify the opening's job, the evidence used, and the next action. Draft the new post from the new brief only. Do not transfer names, results, prices, or product capabilities from the examples. This keeps source facts and style examples in separate roles, which is essential when old posts describe outdated product behavior. ## Add a counterexample with a useful repair A weak example can clarify a boundary when it includes an explanation. Show a generic sentence and a stronger revision, then state why the revision works. Do not simply label one “bad” and the other “good.” For instance, compare “Take your workflow to the next level” with a sentence that names the actual task and the change. The point is not that a particular phrase is forbidden forever. The point is that the second sentence helps a reader understand what to do or expect. Keep counterexamples limited. A prompt dominated by bad writing can become noisy and make it harder to see the intended standard. Choose recurring failures that your team has actually observed, such as unsupported certainty or a call to action unrelated to the post. OpenAI's [general prompting documentation]() supports clear task context and iterative refinement. Treat the example set as one part of that context, not as a replacement for the new assignment's facts. ## Test on a brief the examples do not cover Hold back one realistic task from the example set. Ask ChatGPT to draft it using the examples and the new brief. This tests whether it learned transferable decisions or merely repeated a format. If all examples describe launches, test an educational post about a limitation. If the set includes only text, test the wording for a short [carousel outline](). You are looking for consistent judgment across a different surface, not identical sentence patterns. Review for copied phrases and imported facts. Also check whether the model preserves necessary qualifications. A cheerful voice should not erase a constraint that matters to the reader. Record where the set fails. Perhaps it produces clear explanations but overuses rhetorical questions. Add or revise an example that demonstrates a different opening, then rerun the same held-out task. Keep the input stable so the comparison remains useful. ## Maintain a small library with explicit scope Store the approved examples with labels describing when to use them. A customer education set, a founder perspective set, and a release update set may need different evidence and levels of formality. They can share principles without sharing every sentence pattern. Review the library when product facts change or the brand's editorial direction shifts. Remove outdated claims from examples or label them as historical style references. Otherwise a style prompt can become an unexpected source of stale factual information. Do not add every successful output back into the set. That creates a feedback loop where the model's own repeated habits become the brand standard. Keep human-selected examples and explain why each earns its place. A [content audit]() can help you find strong existing material, but example-library maintenance is a narrower job: preserve a clear teaching set that makes new work easier to judge. ## Keep the final draft accountable to the new brief Before production, compare the draft with the current source material and intended reader action. The example set should improve expression without deciding facts. If a stylish sentence cannot be supported by the new brief, revise it even if it resembles a beloved older post. Move the approved copy into Caroush's [content creation tools]() and inspect the destination-specific presentation. A sentence that works in a long LinkedIn post may need a different layout in a visual carousel or a shorter caption. This is an editorial workflow, not a claim of a verified direct ChatGPT connection to Caroush. The handoff can be manual. The lasting benefit is an example set that teaches clear decisions, stays current, and leaves enough room for each new post to say something worth reading. ## Sources - [Prompt engineering]() - [Prompting]() ## Frequently asked questions ### How many examples should I use? Start with a small set that covers the intended writing tasks. Add an example only when it teaches a distinct decision the current set misses. ### Should the best-performing posts become the examples? Performance can provide context, but select examples for clear writing, factual accuracy, and representative tasks rather than engagement alone. ### Can examples contain old product facts? Prefer current examples. If historical material is necessary, label it as a style reference and explicitly prohibit transferring its facts. ### What is a held-out writing test? It is a realistic brief not included in the examples, used to check whether the assistant applies the principles to new work. ## About the author Garry Gaurav Sapkota builds Caroush, a workspace for creating, scheduling, and publishing social content. - [https://x.com/gauravsapkotanp]()