# Sycophantic opener Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: sycophantic-opener - Category: Model register (model-register) - Subcategory: assistant-register - Also known as: great question, affirmation preamble, assistant flattery, captatio inverted, sycophantic praise register, validate-then-restate opener, That's a fair point, and, hollow acknowledgment pivot, what a sharp observation - Status: Active. Currently signals low-effort writing. - Severity: high - Evidence grade: peer-reviewed (a published study measures the pattern) - Languages: en - Added: 2026-08-14 - Updated: 2026-08-17 - Page: https://feedsquad.com/ai-tells/sycophantic-opener ## Description The first sentence praises the reader or the question before any content arrives. It carries no information. Delete it and the text loses nothing, which is the test. In a published post it is also addressed to the wrong person, because the reader never asked anything. ## Why it reads as machine-written Assistant register is trained in. The ICLR 2024 sycophancy study analysed a 15,000-comparison preference dataset and found matching a user's beliefs among the most predictive features of human preference, with the behaviour present in assistants from three vendors. A later study measured models preserving the user's face 45 percentage points more often than humans on advice queries. The opener is what that reward looks like at the top of a paragraph. ## Detection Type: deterministic (a regular expression, run as written) Scope: sentence ECMAScript regular expression. The build validator compiles it and tests it against this entry's own specimens. ```regex ^[ \t]*(?:(?:certainly|absolutely|of course)[ \t]*[!.]|(?:you(?:'|’)?re absolutely right|great question|excellent question|what a (?:great|fantastic) question|i hope this helps)\b) ``` Flags: im ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > Great question! You're absolutely right that timing matters more than most people think. Consistency is what separates a feed that builds an audience from one that simply exists, and it is usually the easiest of the variables to control. Cadence decides a great deal more than it gets credit for. After, repaired: > Timing mattered less than we expected. We moved our posting slot four times over six months, and the gap between the best and worst slot was smaller than the gap between two posts published in the same slot on different weeks. Note: The opener goes and something testable replaces it. Deleting the opener on its own would leave a shorter post with the same emptiness. ### Specimen 2 Before, exhibiting the tell: > Certainly! Onboarding is a fantastic area to focus on. The first week sets the tone for everything that follows, and treating it as an afterthought is the sort of decision that gets paid for later rather than avoided. Get the beginning right and a great deal of the rest follows without much argument. I hope this helps as you think through your next steps. After, repaired: > Start by counting where people stop. Ours stopped at the ninth form field, which we only found because we logged partial submissions for a week. Note: Both the greeting and the closing offer of help are addressed to a prompter who does not exist on the page. ## False positives Who legitimately writes this way. Affirmation openers are trained behaviour in customer-facing work. Support agents are scripted to acknowledge before answering and are measured on it, and sales and hospitality staff are trained the same way. Teachers open with encouragement because the alternative discourages the student, and this is explicit practice in feedback rubrics. Several varieties of English treat an affirming opener as ordinary politeness, and speakers taught English through formal correspondence carry that habit into posts and comments. The regex requires the phrase at the start of a line or sentence, which keeps 'she asked a great question' out. Even so, the tell is the opener plus the absence of anything the answer needed. On its own it means somebody was being pleasant. ## Model attribution Documented across assistants from three vendors rather than in one family, and traced to preference data rather than to architecture. Anthropic's dated product system prompts instruct against adjacent habits, including the words genuinely, honestly and straightforward, which is evidence of the underlying pull and not of what any deployed product emits. OpenAI's April 2025 GPT-4o update was rolled back for the same class of behaviour. ## Platform notes - linkedin: The comment surface is where this concentrates, and LinkedIn's first named enforcement target is comments created at scale by automation with minimal human involvement. - x: Reply scoring is a separate published module from the post classifier, and the RiskyHighVizReply label used for slop posts is also written by the reply-spam path. Reply-guy affirmation under large accounts is the archetypal target. - wikipedia: The editor guide lists the exact strings under communication intended for the user, including 'Of course!', 'Certainly!', 'You're absolutely right!' and 'I hope this helps'. Most sightings are on talk pages and in edit summaries rather than in articles. ## Sources 1. Wikipedia:Signs of AI writing (section: Communication intended for the user; Collaborative communication) https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing (tier: community; accessed 2026-08-14) 2. Sharma et al. Towards Understanding Sycophancy in Language Models. ICLR 2024 https://arxiv.org/abs/2310.13548 (tier: peer-reviewed; accessed 2026-08-14) 3. Cheng, Yu, Lee, Khadpe, Ibrahim, Jurafsky. ELEPHANT: Measuring and understanding social sycophancy in LLMs (arXiv preprint, not peer reviewed) https://arxiv.org/abs/2505.13995 (tier: primary-doc; accessed 2026-08-14) 4. Anthropic: Claude system prompt release notes (dated product system prompts) https://platform.claude.com/docs/en/release-notes/system-prompts (tier: vendor; accessed 2026-08-14) 5. Wikipedia: Signs of AI-generated comments https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI-generated_comments (tier: community; accessed 2026-08-14) 6. slop-lint, social-reply-register molds https://github.com/eric-sabe/slop-lint (tier: community; accessed 2026-08-14) 7. vale-ai-tells, 111 machine-checkable rules https://github.com/tbhb/vale-ai-tells (tier: community; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-14, Active: Backed by peer-reviewed work on sycophancy as an RLHF artifact and by the Wikipedia editor guide's verbatim string list under communication intended for the user. Kept in the highest-precision family because the text is addressed to a prompter rather than a reader. Regex verified on 2026-08-14 against four positive and five negative strings; the negatives include the sentence-initial 'Of course the tradeoff is cost', which the punctuation requirement excludes. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.