Quick take: A pseudo-code prompt framework analyzes an author’s style across rhythm, vocabulary, formality, and sentence structure, then replicates those patterns in fresh content without lifting a single original line.
What This Prompt Actually Does
Most “write like [author]” prompts fail because they target surface-level patterns. The result sounds like a parody. This framework from u/Ornery-Dark-5844 on r/PromptEngineering takes a different approach: it decomposes writing style into discrete, measurable signals before generating anything.
The system is built around eight cores, each handling a specific layer of the process:
- 🖊️ Style Analysis — extracts sentence length, formality, metaphor usage, and rhetorical question frequency from a sample corpus
- Pattern Reproduction — maps paragraph openings, idea transitions, and sentence complexity
- Vocabulary Adaptation — adjusts word choice based on whether the style is simple, technical, or metaphorical
- Narrative Rhythm — identifies the base rhythm (fast, reflective, descriptive, analytical) and locks it throughout generation
- Anti-LLM Patterns — explicitly bans template intros, excessive sentence symmetry, repetitive connectors, and suspiciously perfect lists
- Generation Pipeline — runs in three steps: tonal opening, body development, natural conclusion
- Quality Control — checks for voice consistency and loops back to regenerate if the output reads as AI-produced
- Output Spec — targets text that is human-authored in feel, stylistically coherent, and rhythm-consistent
The Anti-LLM core is what makes this worth paying attention to. Most prompts tell the model what to do. This one tells it what to stop doing first. That inversion matters more than it sounds. LLMs default to comfortable patterns: the transitional “Furthermore,” the perfectly balanced three-item list, the conclusion that begins “In summary.” Naming those patterns explicitly and banning them forces the model out of its default groove and into something that actually resembles how a specific human thinks on the page.
The Style Analysis step is equally important. Feeding in three to five paragraphs from your target author gives the model enough signal to extract rhythm without over-indexing on any single quirk. Short samples produce mimicry. Larger, varied samples produce internalized style. The difference shows up immediately in the output.
Use Cases
- Ghostwriting: train the model on a client’s existing writing before drafting new content in their voice. Even a handful of their past emails or articles is enough to build a usable style profile.
- Content scaling: maintain a consistent authorial tone across a team or across months of output. Useful when multiple writers contribute to a single brand voice and consistency matters.
- Style study: run this on authors you admire to deconstruct what actually makes their writing work. You will often discover things you noticed intuitively but could never name, like a preference for concrete nouns over abstract ones, or a tendency to end paragraphs with a short punchy sentence.
- Newsletter writing: match your established voice instead of defaulting to generic AI cadence. If you have been publishing for a year, your archive is the corpus.
Prompt of the Day
Here is the condensed version you can drop into any long-context model. Feed it a writing sample first, then give it a topic:
OBJECTIVE: Emulate a specific human author's writing style. STEP 1 , ANALYZE STYLE Given [paste writing sample here], extract: - Average sentence length and rhythm - Formality level (scale 1-5) - Metaphor and analogy frequency - Rhetorical question usage (yes/no) - Paragraph opening patterns - Preferred sentence structure (short / long / mixed) STEP 2 , BUILD STYLE PROFILE Store the above as a style profile. Do not generate text yet. STEP 3 , GENERATE Write about [your topic] applying the style profile. Rules: - Match sentence rhythm and length patterns from the profile - Adapt vocabulary to the author's register - Use organic transitions, not template connectors - Avoid: template intros, perfect structural symmetry, repetitive conjunctions, bullet lists (unless the author uses them) - Conclusion type: natural / reflective / open-ended (match author's preference) STEP 4 , QC CHECK Before outputting, verify: - Consistent author voice throughout? - Any LLM-sounding phrases? If yes, rewrite those sections. - Any connector word repeated more than twice? If yes, vary it. OUTPUT: Final text only. No meta-commentary.
The separation between Step 2 and Step 3 is intentional. Asking the model to analyze and generate simultaneously collapses the process and degrades output quality. Making it pause, build the profile, and only then write gives the generation step a richer internal reference to work from.
One Thing Worth Noting
The original prompt is written in Portuguese with a pseudo-code notation. GPT-4o and Claude handle it well. Smaller models may need the syntax converted to plain English instructions. The underlying logic holds either way. If you hit inconsistent results, the most reliable fix is expanding the corpus. More writing sample, better profile, cleaner output. Quality beats quantity, but you generally need at least 200 to 300 words of source material to get a result that feels like a real voice and not just a surface impression.
Give It a Run
Pick three paragraphs from a writer you want to sound like. Feed them in as the corpus. Ask the model to write something in that voice on a topic you actually care about. Then compare the output to what you normally get from a plain “write like [name]” instruction.
If the first result still feels generic, check whether the sample you fed in was varied enough. Three paragraphs from the same section of one article will produce a narrower profile than three paragraphs pulled from different pieces written in different contexts. Variety in the input creates range in the output.
The difference tends to be obvious immediately.
Emulação Estilo Autor Humano
by u/Ornery-Dark-5844 in PromptEngineering