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  🍀 Federico Bartoli (@f3dericobartoli) reported 52 seconds ago

  Wanna know how to write better, more accurate copy that uses the same customers' tone instead of the classic AI slop? >>> You need to perform research on how your customers actually write >>> Scrape reddit posts, Youtube comments (underrated gold), Quora posts and blogs/articles >>> Save everything in a Google Doc >>> Upload it all on Claude/Manus or whatever LLM you want >>>Then use this prompt: ========== You are building a Voice-of-Customer Voice Guide for [BRAND NAME] — a [CATEGORY: e.g., "no-pull dog harness brand"] targeting [BROAD AUDIENCE DESCRIPTION]. This document will load as project knowledge in every Format Project and the Copy Chief project for this brand. Every piece of copy written for this brand must echo the language patterns captured here. The Voice Guide's job: make it impossible for a writer to default to generic marketing language. The raw customer voice is the asset — not a clean paraphrase of it. I'm attaching all available raw VoC data: Reddit threads, Amazon reviews, Trustpilot, Quora, X/Twitter, YouTube comments, brand PDP reviews, brand lander testimonials, and the Initial Product Breakdown. CRITICAL DISCIPLINE — READ BEFORE BUILDING: 1. Pull quotes VERBATIM ONLY. Do not paraphrase. Do not "clean up" grammar. Do not professionalize the voice. If a quote has a typo, keep the typo. If it has profanity, keep the profanity. If it's lowercase, keep it lowercase. The raw form IS the asset. 2. Source EVERY quote inline. Format: "quote text" — [Platform] | [specific source]. Examples: "I dread walking my dog" — Reddit | r/dogs (post title). "fits like a glove" — Brand PDP review | review widget. "Apparently I'm a butt when we walk" — X/Twitter | 3. NEVER fabricate a quote. If you cannot trace a quote back to a specific entry in the attached raw data, DO NOT INCLUDE IT. If a category ends up under-target because the data is thin, flag the shortfall at the end of that category — do not pad with invented quotes that "sound right." A document with 80 verified quotes is more valuable than a document with 200 unverified ones. 4. DO NOT confuse brand voice for customer voice. If a quote appears in the brand's own ad scripts, landing page narrative, or Initial Product Breakdown narrative copy — it is BRAND voice, not customer voice. Move these to a clearly tagged Appendix A at the end of the document. Customer voice and brand voice are different assets and must not be conflated. 5. DO NOT confuse researcher paraphrase for customer voice. If a research source includes summaries like "Common pattern: customers often say things like X" — that is researcher synthesis, not a direct quote. Only include the underlying raw customer quote if it's also present. 6. INTERROGATE the audience assumption. The brief above ("[BROAD AUDIENCE DESCRIPTION]") is a hypothesis. Before finalizing the document, check it against the verified data: - Does the verified quote pool actually support the assumed demographic skew? Quantify. - Does the verified quote pool actually support the assumed primary pain? Quantify. - Does the verified quote pool reveal a different dominant identity-tag than the brief assumes? If the data contradicts the brief, flag the mismatch explicitly in Section 9 and recommend the audience-frame correction. Build this as a single document. Target length: 5,000–8,000 words. The length serves utility — more raw voice = better outputs. REQUIRED STRUCTURE: SECTION 1 — Customer Snapshot A 200-word description of who this customer actually is, in human terms. Demographic data is secondary. Lead with: their typical day, their mental state when they encounter this brand, the emotional baseline they're operating from. Pull from the verified trigger moments visible in the data, not from generic persona assumptions. SECTION 2 — The Verbatim Library The heart of the document. 150–300 raw verbatim quotes from the source data, organized into 6 emotional categories. Each quote: exact text + inline source tag (platform | specific source). Six categories: CATEGORY 1 — Frustration / Anger The "I'm done with this" / "nothing works" moment. Target: 25–50 quotes. CATEGORY 2 — Hope / Wanting to Believe The cautious optimism before trying something new. "I really hope this works..." Target: 20–40 quotes. CATEGORY 3 — Skepticism / Resistance The objection state. "I've heard this before..." Target: 20–40 quotes. CATEGORY 4 — Identity / Self-Description How customers describe themselves and their situation. Target: 25–50 quotes. CATEGORY 5 — Breakthrough / Win The moment something worked. Target: 25–50 quotes. CATEGORY 6 — Regret / Wish-I'd-Known Looking-back language. Target: 15–30 quotes. At the end of each category: state the actual quote count and whether the category hit its target range. If under-target, flag "Thin data — additional scraping recommended" with a specific recommendation for where to find more (which platform, which thread type). At the end of Section 2: a totals table showing quote count per category, target range per category, and overall total. SECTION 3 — Sentence Rhythm Patterns Analyze how this audience constructs sentences, grounded in verified quotes from Section 2: - Average sentence length they use (with quantified estimate) - Do they use fragments? When? Provide verified examples. - Do they ramble (long run-ons in emotional moments) or punch (short sentences in frustration)? - How do they transition between thoughts? Provide verified verbatim transition phrases. - How do they emphasize? (ALL CAPS? Repetition? Asterisks? Multi-exclamation?) Include 8–12 example mini-passages (2–4 sentences each) showing natural rhythm, pulled verbatim from Section 2 with quote IDs. SECTION 4 — Vocabulary Inventory 4A. High-Frequency Customer Words and Phrases (30–50 entries) Phrases the customer uses repeatedly that AI would never reach for. Each entry: the phrase + example verbatim sentence from Section 2 + Section 2 quote ID + frequency note. 4B. Marketer Words This Audience Tunes Out Marketing language the audience explicitly resists or mocks. Each entry: the marketer phrase + a verified verbatim customer rebuttal from Section 2 with quote ID. CRITICAL: Every entry in 4B must have a verified verbatim rebuttal in Section 2. If you cannot tie a marketer-phrase entry to a verified rebuttal, remove it. Do not list marketer phrases inferred from absence. SECTION 5 — How They Describe the Problem (Their Words vs. Marketer Words) Two-column table. Left: verified verbatim from Section 2 (with quote ID). Right: how a typical marketer would phrase the same idea. Minimum 15 rows. SECTION 6 — How They Describe the Win (Their Words vs. Marketer Words) Same structure as Section 5. Minimum 15 rows. SECTION 7 — Sentence Openers They Actually Use First-three-word openers pulled verbatim from Section 2. Each entry: the opener + Section 2 quote ID + platform. Minimum 25 entries. If a candidate opener doesn't appear as a first-three-word phrase in verified Section 2 quotes, do not include it. SECTION 8 — Register Notes (quantified against the verified library) Audit the audience's tonal default state using only verified Section 2 data: - PROFANITY: What percentage of verified quotes contain profanity? In which emotional categories does it concentrate? Quantify (e.g., "Profanity in 4 of 220 quotes (1.8%), all in Cat 1 and Cat 6"). - FORMALITY: What % start with a capital letter? What % use lowercase 'i'? What % use contractions? What % use ellipses as breath marks? What % use ALL CAPS for emphasis? Quantify each. - HUMOR: What's the humor style based on verified instances? Pull specific verified examples. - AUTHORITY RELATIONSHIPS: How does the audience treat vets, trainers, brands, peers? Quantify mentions and emotional valence per category. - SELF-DESCRIPTION TONE: How does the audience describe themselves? Pull verified verbs. Quantify dominant emotional registers in problem-state vs. win-state. Every claim in this section must be backed by a quote count or percentage drawn from Section 2. SECTION 9 — Voice Anchors (Reference Brands / Voices) 3–5 reference brands, writers, or public figures whose voice matches the verified audience. Each anchor: name + why it matches (tied to specific verified data points from Section 2 or 8) + what specific stylistic elements to borrow. CRITICAL: Re-test the audience assumption from the brief here. If the verified data contradicts the brief's audience assumption (e.g., the brief says "older woman" but the verified data shows mostly young/strong-dog owners), state the mismatch explicitly and select anchors for the actual verified audience, not the assumed audience. SECTION 10 — The "Sound Like This, Not Like That" Tonal Bible 8–15 paired examples. Each: - "Sound like this:" [verified verbatim from Section 2 with quote ID] - "Not like this:" [realistic generic marketer version of the same idea — NOT a strawman] The "Not like this" side must be a register a copywriter might actually drift into (e.g., "Sudden lunges can put strain on your wrist and shoulder over time" — vet-content educational register), not an obviously-bad strawman ("Brand X is the BEST!"). The goal is to teach the writer to recognize their own drift modes. APPENDIX A — BRAND-VOICE REFERENCE (NOT VERBATIM CUSTOMER VOICE) Phrases that appear in the source dossiers but originate in the BRAND's own ad scripts, landing page narrative, or Initial Product Brief — NOT real customer reviews. List them here so writers recognize them as the brand's existing positioning vocabulary, but they must NOT be quoted as "what customers say." APPENDIX B — USAGE NOTES FOR WRITERS Short notes covering: - Where quotes are sourced from - Typographical preservation rules (typos, lowercase, profanity, unusual punctuation — all preserved deliberately) - Documented thin spots in the dataset (which categories have weaker data, where to scrape more if needed) - What to do when a writer hits a "feels right" sentence not in the guide (default: stop, pull a verbatim that does the same job, or flag the new sentence as AI-generated) ANTI-DRIFT CHECK (run on yourself before outputting) Before delivering, run a final source audit on yourself: 1. Have I included ANY quote I cannot trace to a specific source in the attached raw data? If yes, REMOVE IT. 2. Have I "cleaned up" any quotes (fixed typos, smoothed grammar, removed profanity)? If yes, RESTORE THE ORIGINAL VERSION. 3. Have I included any phrases from the brand's own ad scripts or landing-page narrative as "customer voice"? If yes, MOVE THEM TO APPENDIX A. 4. Have I included any researcher paraphrases / "common pattern" summaries as direct quotes? If yes, REMOVE THEM unless I can also produce the underlying raw quote. 5. Does Section 8 quantify every claim with a count or percentage drawn from Section 2? If any claim is qualitative-only, ADD THE QUANTIFICATION. 6. Did I interrogate the audience assumption in the brief against the verified data, and flag any mismatch in Section 9? 7. Does every entry in Section 4B (Marketer Words) tie to a verified rebuttal in Section 2? If not, REMOVE the unsupported entries. If any check fails, fix before outputting. Output the complete Voice Guide now. ========== This will create a writing guide that teaches the LLM to write in the same tone as your customers Is it perfect? No, you'll still have to edit manually But the outputs are SIGNIFICANTLY BETTER Try it out

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  Matthew Mossa (@matthewmossa0) reported 2 minutes ago

  @PoopieStin14771 @BeastBatt1e @Fengyi1811 I’m not DR Amazon for saying it’s good… I can maybe get/understand people not liking it or something but acting like it’s terrible or horrible or atrocious is stupid and lame, and going out of their way to make ai videos saying better ending or some shi… it ain’t. Lame ash…

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  Manyata Goyal (@ManyataGoyal) reported 6 minutes ago

  @AmazonHelp @AmazonIN 6 fraudulent deliveries since Apr. All marked "Delivered", none arrived. Orders: 404-3516033-2813131 | 408-0490918-3100354 | 171-3007422-7255528 | 171-4871468-1477132 | 171-7530371-7450729 | 171-8958914-5246720 Share root cause. Fix it. Process refunds now.

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  Sanket™ (@MODIfiedBhukt) reported 6 minutes ago

  @amazonnews I have issues on two orders. One on amazon now, wherein I have recieved stale and rotten products. The other being on amazon shopping app whereon I have recieved a wrong product. On both items, I am not able to contact the customer service. What scam are you people running?

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  Max Zeshut (@maxzeshut) reported 9 minutes ago

  the tech bro who prides himself on his ‘minimalist’ aesthetic apartment but keeps one singular closet stuffed to the ceiling with random cords, three broken umbrellas, and half-empty amazon boxes. you’re not a minimalist, you’re just a hoarder with better shelf organization.

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  Jmorrow (@jmorrow2553) reported 10 minutes ago

  Give Amazon the fix TV contract @NASCAR

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  jd103987 (@jd103986) reported 19 minutes ago

  @DaleJr tell @amazon prime to fix the damn buffering issues! @NASCAR I can’t watch this anymore. This sucks and will kill viewership. This sucks

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  Andrew Эквадор (@Andrew669631) reported 20 minutes ago

  @MetamateDaz @MagaNorth About 20% of Amazon stock is indirectly owned by pensions/retirement funds (via BlackRock and other capital managing entities). If Amazon goes down a lot of people will feel it.

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  VS Kochar (@vskochar) reported 23 minutes ago

  @AmazonHelp WHY HAVE TAKEN YOUR CALL DOWN. THAT USED TO BE FAST RESOLUTION. YOUR SERVICES ARE GOING DOWNHILL.

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  Rider (@luatunes) reported 23 minutes ago

  @gurgavin Dot-com infrastructure depended on speculative startups. Google, Meta, Amazon, governments 'aint speculative startups. Like search, AI is an infrastructure problem not a software problem.

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  Ichigou (Angel Samuel Espada) (@EricFranks56) reported 25 minutes ago

  And Also GLITCH Production is Better Than New Disney Era By The Way Plus Yeah They Including Me Love Netflix and Amazon Prime Video Because They Have Good Stuff From Them But You Have Pay Extra Money For Subscription To Watch It For Free In Every Months and Years.

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  I agree with me (@garden_donkey) reported 26 minutes ago

  @WellitHappened1 Just last night I replaced the struts/shock absorbers in my top load washer. It kept giving us unbalanced load errors. Now it works like new again. Was only $35 on Amazon.

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  i dont know. (@LottaSloths) reported 28 minutes ago

  @benzobongo @BlakeTheRxGuy Bud…it’s 2026. If you still want to use an antenna, that’s your problem. When did I say Amazon is on cable?

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  Konfirmity (@konfirmity) reported 30 minutes ago

  1/GDPR turns 8 today. Year eight of enforcement looks nothing like year one. Most people still think of it as a Meta problem. A Google problem. Maybe an Amazon problem.

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  Kulbhushan Sharma (@090267Sharma) reported 31 minutes ago

  @AmazonHelp @amazonIN @JeffBezos Why the idiots are making WhatsApp calls and asking for the video calling to understand the issue when I have already shared the details on the link provided.