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logo",81,36,[],{"asset":393},[394],{"type":27,"image":395,"mobileImage":401},[396],{"src":397,"alt":398,"width":399,"height":400},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FLogos\u002Flogo-google-partner.svg","Google Partner logo",87,61,[],[403,408,413],{"buttonLink":404},[405],{"ariaLabel":9,"target":9,"url":406,"text":407,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Fprivacy-policy\u002F","Privacy Policy",{"buttonLink":409},[410],{"ariaLabel":9,"target":9,"url":411,"text":412,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Fleapus-csr-policy\u002F","Leapus CSR Policy",{"buttonLink":414},[415],{"ariaLabel":9,"target":9,"url":416,"text":417,"entryType":12},"https:\u002F\u002Fpixis.ai\u002Ffulfillment-policy\u002F","Pixis Fulfillment 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Arora",[433],{"type":27,"image":434,"mobileImage":439},[435],{"src":436,"alt":9,"width":437,"height":438},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002FIMG_9067.jpg",846,925,[],"SEO Specialist","\u003Cp>Janvi is an SEO Specialist at Pixis, working across SEO, generative engine optimization (GEO), and AI search. A Christ University alumna, she focuses on understanding how brand discovery is changing as search expands from ranked results to AI-generated answers. Her work covers content optimization, search visibility, and helping brands adapt to the evolving ways people find information online.\u003C\u002Fp>",{"url":443},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjanviarora\u002F",[],{"title":446,"description":447,"advanced":448,"keywords":451,"social":452},"How to Set AI Content Reading Level and Claim Sensitivity | Pixis","Set reading level and claim sensitivity for AI content in regulated industries: target a grade, build a claims register and scale review to risk.",{"canonical":449,"robots":450},"",[],[],{"facebook":453,"twitter":454},{"description":447,"title":422},{"description":447,"title":422},[456],{"type":27,"image":457,"mobileImage":463},[458],{"src":459,"alt":460,"width":461,"height":462},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fhow-to-set-ai-content-reading-level-and-claim-sensitivity-featured.png","Pixis Visibility claims checked card, a review tier 3 label and a reading level slider set to grade 6, next to a woman smiling at her laptop",1920,1360,[],[465,468,471],{"title":466,"slug":467},"SEO\u002FAEO\u002FGEO","seo-aeo-geo",{"title":469,"slug":470},"Pixis Visibility","pixis-visibility",{"title":472,"slug":473},"Content Marketing","content-marketing",true,[476],{"blocks":477},[478,481,493],{"type":479,"textBlock":480},"textBlock_Entry","\u003Cp>A medical device company asks its AI writing tool for a patient leaflet. The draft comes back at a college reading level and promises results the clinical data does not support. The team fixes the language, and the next draft reads beautifully, but it still says the device is \"the most effective option available.\"\u003C\u002Fp>\u003Cp>Those are two different problems. Reading level decides whether the reader can understand the text. Claim sensitivity decides whether the text says only what the company can prove. Fixing one does not fix the other.\u003C\u002Fp>\u003Cp>In regulated and technical industries, both matter at once, and the right setting for each depends on who is reading. A patient leaflet and a clinician briefing about the same product need the same claim discipline at very different reading levels.\u003C\u002Fp>\u003Cp>This guide explains how to set each control, how to build the claims register that makes claim sensitivity work, and how to design a review workflow proportionate to risk.\u003C\u002Fp>\u003Ch2>Key takeaways\u003C\u002Fh2>\u003Cul>\u003Cli>Treat reading level and claim sensitivity as two independent settings. Plain language can still contain unsupported claims, and careful claims can still be unreadable.\u003C\u002Fli>\u003Cli>Unprompted AI drafts tend to land well above recommended reading levels for patient material. Name the target grade in the prompt and measure every output with a readability formula.\u003C\u002Fli>\u003Cli>Claim sensitivity only works with a claims register: approved wording, do-not-say phrases, substantiation and an approver for every claim.\u003C\u002Fli>\u003Cli>Scale human review to risk. Plain educational content can pass a checklist, while performance and safety claims need full legal or medical review.\u003C\u002Fli>\u003Cli>Pixis Visibility's Strategy Brain lets you set reading level, claim sensitivity, must-include and never-mention terms for drafts; reviewer sign-off stays with your experts.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Why the two settings fail in different ways\u003C\u002Fh2>\u003Cp>Reading level controls sentence length, word choice and how much background the reader needs. When it is wrong, the reader cannot use the content. Claim sensitivity controls whether statistics are sourced, whether language is hedged where evidence is uncertain, and whether the text avoids guarantees and superiority claims. When it is wrong, the company carries legal and regulatory risk.\u003C\u002Fp>\u003Cp>Many teams assume that technical accuracy needs technical language, so they tune one setting and expect the other to follow. The four combinations show why that fails:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Low reading level, high sensitivity:\u003C\u002Fstrong> patient leaflets, consumer finance explainers. Plain words, every claim sourced.\u003C\u002Fli>\u003Cli>\u003Cstrong>High reading level, high sensitivity:\u003C\u002Fstrong> clinician briefings, regulatory submissions, analyst notes. Technical depth, every claim sourced.\u003C\u002Fli>\u003Cli>\u003Cstrong>Low reading level, low sensitivity:\u003C\u002Fstrong> general awareness content with no product or outcome claims, such as a glossary entry.\u003C\u002Fli>\u003Cli>\u003Cstrong>High reading level, low sensitivity:\u003C\u002Fstrong> internal technical documentation that makes no external claims.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>An illustrative example: a healthcare team writes a patient leaflet and a clinician briefing for the same trial. The leaflet targets roughly a 6th-grade level and the briefing a postgraduate level. Both use high claim sensitivity, so both cite the same approved trial result with the same qualifiers. Only the vocabulary and sentence structure change.\u003C\u002Fp>\u003Ch2>Step 1: Define the reader and the reviewers\u003C\u002Fh2>\u003Cp>Start by classifying each content type by its reader: practitioners, buyers or the public. Each needs a different reading level, and each carries a different level of claim risk.\u003C\u002Fp>\u003Cp>For public health content, the target is well documented. The US Department of Health and Human Services recommends that patient education materials be written at a 4th to 6th grade reading level, as noted in \u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC13097565\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">this study of LLM readability\u003C\u002Fa>.\u003C\u002Fp>\u003Cp>Then name the people who will sign off: the subject matter experts who check accuracy, and the legal or compliance reviewers who check claims. Decide this before any drafting starts, so no AI draft is waiting for an owner.\u003C\u002Fp>\u003Cp>Before configuring anything, it also helps to decide \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-much-brand-context-does-ai-content-need\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">how much brand context AI content needs\u003C\u002Fa> for each content type.\u003C\u002Fp>\u003Ch2>Step 2: Set and verify the reading level\u003C\u002Fh2>\u003Cp>Choose one readability formula and use it for everything, so scores are comparable over time. The common options are Flesch-Kincaid Grade Level, the SMOG Index and the Gunning Fog Index. Each turns sentence length and word complexity into a US grade-level score.\u003C\u002Fp>\u003Cp>Then name the target grade in the prompt or tool settings. Do not rely on the model's defaults. One \u003Ca href=\"https:\u002F\u002Fpmc.ncbi.nlm.nih.gov\u002Farticles\u002FPMC12670965\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">peer-reviewed study of AI-generated patient information\u003C\u002Fa> found that unprompted outputs from ChatGPT, Gemini and Copilot all exceeded recommended readability thresholds, with average Flesch-Kincaid scores between about 11 and 12. The differences between the three models were not statistically significant: all three were similarly too complex.\u003C\u002Fp>\u003Cp>The second study, linked above, prompted the same three models to write at a 5th-grade level and found that the prompted outputs scored significantly lower than unprompted ones. Naming the target works, but it still needs checking.\u003C\u002Fp>\u003Ch3>Check the score before review\u003C\u002Fh3>\u003Cp>Run every draft through your chosen formula before it goes to a reviewer. If it misses the target, ask for shorter sentences and plainer words, and name the terms that can stay technical.\u003C\u002Fp>\u003Cp>An illustrative example: a draft scores 11.4 with an average of 28 words per sentence. After a revision prompt that asks for sentences under 15 words and plain alternatives to five named terms, it scores 6.2. Your own numbers will differ, but the method is the same.\u003C\u002Fp>\u003Cp>Readability formulas measure surface features, not comprehension. Use them as a gate, and use real reader feedback where you can get it.\u003C\u002Fp>\u003Ch2>Step 3: Build the claims register\u003C\u002Fh2>\u003Cp>The claims register is what turns \"be careful with claims\" into something a writer, a reviewer or an AI tool can follow. It lists every claim the organization has approved, in the exact words it is allowed to use.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Claim type:\u003C\u002Fstrong> Performance, safety, pricing, comparative or general description.\u003C\u002Fli>\u003Cli>\u003Cstrong>Approved wording:\u003C\u002Fstrong> The exact sentence with its required qualifiers.\u003C\u002Fli>\u003Cli>\u003Cstrong>Do-not-say:\u003C\u002Fstrong> Common overstatements of the same claim.\u003C\u002Fli>\u003Cli>\u003Cstrong>Substantiation:\u003C\u002Fstrong> The supporting document and its date.\u003C\u002Fli>\u003Cli>\u003Cstrong>Approver:\u003C\u002Fstrong> Reviewer name and approval date.\u003C\u002Fli>\u003Cli>\u003Cstrong>Review-by date:\u003C\u002Fstrong> Shorter for performance claims, longer for plain descriptions.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Next to the register, keep a banned-phrases list. Typical entries include \"guaranteed returns,\" \"the best,\" \"the only,\" \"fastest\" and \"proven cure.\" They imply absolute guarantees or superiority without evidence, which is where regulatory risk sits in healthcare, finance and legal content. Add anything your legal team flags.\u003C\u002Fp>\u003Cp>New or changed claims go through legal or compliance review before they enter the register. That is what makes later review fast: reusing approved wording word for word needs only a checklist, while anything new gets a full review.\u003C\u002Fp>\u003Ch2>Step 4: Configure high claim sensitivity\u003C\u002Fh2>\u003Cp>High claim sensitivity tells the AI to favor accuracy over bold statements. In practice it means four instructions, applied to every draft:\u003C\u002Fp>\u003Col>\u003Cli>Source every statistic, or leave it out.\u003C\u002Fli>\u003Cli>Use hedged language where the evidence is uncertain.\u003C\u002Fli>\u003Cli>Use performance numbers only with the approved metric, period, population and qualifier from the register.\u003C\u002Fli>\u003Cli>Never use guarantees, superiority claims or any phrase on the banned list.\u003C\u002Fli>\u003C\u002Fol>\u003Cp>Anything that does not meet those rules becomes an exception for review, not a published sentence. A comparative claim such as \"best\" or \"only\" goes to the claim's owner.\u003C\u002Fp>\u003Cp>The \u003Ca href=\"https:\u002F\u002Fpharmacystandards.org\u002Fcaidra-examination\u002Fsection-14-4-compliance-considerations-for-ai-generated-content\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">Council on Pharmacy Standards\u003C\u002Fa> puts the principle this way: \"The governance question is not whether a human or an algorithm typed the sentence. The defensible question is whether the organization can show an appropriate, risk-based system of controls for the communication, data, technology, reviewers, and changes over time.\"\u003C\u002Fp>\u003Cp>The same discipline also helps outside regulated content. Grounded, sourced claims are part of \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fblog\u002Fhow-to-create-ai-content-that-ranks-and-gets-cited\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">creating AI content that ranks and gets cited\u003C\u002Fa>.\u003C\u002Fp>\u003Ch2>Step 5: Design review proportionate to risk\u003C\u002Fh2>\u003Cp>The \u003Ca href=\"https:\u002F\u002Fwww.ema.europa.eu\u002Fen\u002Fdocuments\u002Fother\u002Fguiding-principles-use-large-language-models-regulatory-science-medicines-regulatory-activities_en.pdf\" rel=\"noopener noreferrer\" target=\"_blank\">EMA's guiding principles on large language models\u003C\u002Fa> are direct: \"review all LLMs outputs for veracity, reliability and fairness,\" and \"the greater the risk of potential harm is, the greater the degree of scrutiny must be.\"\u003C\u002Fp>\u003Cp>A practical way to apply that is three review tiers:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Tier 1, checklist review:\u003C\u002Fstrong> educational content that reuses approved wording only. One reviewer confirms the reading level score and checks claims against the register.\u003C\u002Fli>\u003Cli>\u003Cstrong>Tier 2, expert review:\u003C\u002Fstrong> technical content with new explanations but no new claims. A subject matter expert checks accuracy.\u003C\u002Fli>\u003Cli>\u003Cstrong>Tier 3, full legal and medical review:\u003C\u002Fstrong> any new performance, safety, pricing or comparative claim. Nothing publishes until both sign off.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The Council on Pharmacy Standards notes that human-in-the-loop review can take several forms, including pre-release approval, exception handling, escalation and periodic sampling. It also warns that sampling alone may be inadequate when an error could cause significant patient, legal or promotional harm. Tier 3 content should always get pre-release approval.\u003C\u002Fp>\u003Cp>Keep a record of each draft's prompt, the reviewer's decisions and any corrections. That record is how you show the risk-based system the quote above describes.\u003C\u002Fp>\u003Ch2>Where Pixis Visibility fits\u003C\u002Fh2>\u003Cp>The repeat work here is restating the same constraints in every prompt and hoping each writer remembers them. Setting them once is where software helps.\u003C\u002Fp>\u003Cp>In \u003Ca href=\"https:\u002F\u002Fpixis.ai\u002Fproducts\u002Fpixis-visibility\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">Pixis Visibility\u003C\u002Fa>, Strategy Brain holds the brand context that drafts are written from. You can upload a brand book, set voice settings including reading level and claim sensitivity, and define must-include terms and never-mention terms, which is a natural home for your banned-phrases list. Drafts produced from your content plan and templates then start from those settings rather than from a blank prompt.\u003C\u002Fp>",{"type":482,"asset":483,"assetWidth":492},"asset_Entry",[484],{"type":27,"image":485,"mobileImage":491},[486],{"src":487,"alt":488,"width":489,"height":490},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fpixis-visibility-strategy-brain-knowledge-base.jpg","Pixis Visibility Strategy Brain Knowledge Base section for brand persona, tone and reading level, with brand guideline documents added as brand examples",1440,778,[],"large",{"type":479,"textBlock":494},"\u003Cp>When a draft is ready, one-click WordPress publishing pushes it to WordPress as a draft first, and going live needs approval, which fits a review gate. Workspace member roles let you control who can do what.\u003C\u002Fp>\u003Cp>What stays with your team is the part regulators care about: maintaining the claims register, checking every output against it, measuring the reading level, and signing off.\u003C\u002Fp>\u003Ch2>Start with one content type and one register\u003C\u002Fh2>\u003Cp>Pick the content type with the highest claim risk, often patient or customer-facing material. Write down its target reader, reading level and review tier, and build a claims register for the ten claims it uses most.\u003C\u002Fp>\u003Cp>Run the next three drafts through the full process and note where they fail. Those failures tell you which instructions to tighten before you scale the setup to other content types.\u003C\u002Fp>\u003Cp>Get started with \u003Ca href=\"https:\u002F\u002Fvisibility.pixis.ai\u002F\" rel=\"noopener noreferrer\" target=\"_blank\">Pixis Visibility\u003C\u002Fa> to set reading level, claim sensitivity and never-mention terms once and apply them to every draft.\u003C\u002Fp>\u003Ch2>FAQs\u003C\u002Fh2>\u003Ch3>Can AI write reliably at a specific reading level?\u003C\u002Fh3>\u003Cp>It can get close when you name the target grade, but results vary by model and topic. Treat the target as a starting point, measure every draft with the same readability formula, and revise anything that misses.\u003C\u002Fp>\u003Ch3>What is the difference between a claims register and a style guide?\u003C\u002Fh3>\u003Cp>A style guide covers tone, terms and formatting. A claims register lists the specific product or performance claims the organization has approved, with exact wording, substantiation and an approver. AI content in regulated industries needs both.\u003C\u002Fp>\u003Ch3>How often should a claims register be reviewed?\u003C\u002Fh3>\u003Cp>Set a review-by date for every entry. Performance and outcome claims need shorter intervals because the underlying evidence changes, while plain product descriptions can be reviewed less often. Any change to the substantiation should trigger a review straight away.\u003C\u002Fp>",[],{"blogBanner":497},{"bannerIcon":498,"bannerBrandName":503,"bannerHeading":508,"bannerCta1Text":509,"bannerCta1Url":79,"bannerCta2Text":510,"bannerCta2Url":259},[499],{"url":500,"title":501,"width":502,"height":502},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fv-logo.svg","V logo",78,[504],{"url":505,"title":506,"width":507,"height":60},"https:\u002F\u002Fd191k2rrohvvg6.cloudfront.net\u002Fimages\u002Fvisibility-logo.svg","Visibility logo",191,"Increase your visibility on Google and AI search","Book a demo","Signup for free",1793384444219]