ChatGPT can do more than write captions. Used right, it can study your past posts, mirror your brand voice, pull ideas from customer reviews, and build platform-specific drafts (with hooks, alt text, hashtags, and a test plan). This guide shows you how—with research-backed prompts you can paste and run today.

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Why use ChatGPT for social (and what to avoid)

  • Speed + structure: Batch a week of on-brand posts in 30–45 minutes.
  • Consistency: Lock tone, reading level, and CTAs across platforms.
  • A/B learning: Generate variants and log UTMs so you can measure what wins.
  • Avoid: generic “inspirational” fluff, made-up stats, and inaccessible images. Always add good ALT text and platform context. See W3C image description guidance and X/Twitter’s alt-text help.

Step 1 — Give ChatGPT your baseline (so it sounds like you)

Paste the following once in a new chat. It stores your brand voice, goals, and constraints.

Step 2 — Make ChatGPT “dig” (analyze what already works)

Don’t guess. Feed it your actual posts and metrics. Copy/paste last month’s top 10 posts with likes, comments, saves, link clicks. Then run:

Step 3 — Generate platform-specific drafts (IG, FB, TikTok, LinkedIn)

Each platform favors different structures. Use prompts that force the right output pieces and accessibility:

Instagram / Facebook

TikTok

LinkedIn (owner/founder POV)

Step 4 — Accessibility baked in (ALT text that helps real people)

Add meaningful, concise ALT text for images (describe the content and its purpose, not the aesthetics). See W3C’s tutorials for when to include details and when to keep it brief; here’s how to add ALT on X/Twitter.

Step 5 — Measure with UTMs (so you learn what works)

When you link to your site, add UTM tags so you can see which post drove the click in your analytics. The easiest way: Google’s Campaign URL Builder. Save a few standard templates and reuse them.

Step 6 — Bring in real customer language (reviews, chats, emails)

Paste anonymized snippets from reviews or support emails. Have ChatGPT pull pains, desires, and exact phrases for hooks and captions.

Step 7 — Build your weekly system

  1. Mon (Plan): Run Prompt 2, pick 5 posts, create UTM links, schedule.
  2. Wed (Make): Record short videos (Prompt 5); export captions/ALT (Prompts 3 & 7).
  3. Fri (Measure): Check GA for UTM clicks and top posts; note saves/shares.
  4. Sun (Refine): Keep what worked, drop what didn’t, iterate hooks.

Worked examples (swap in your business)

Example A — Lake Havasu plumber (Instagram carousel)

  • Hook: “3 leaks that spike Havasu water bills (and how to spot them).”
  • Body: 6 slides (problem → fix). Local tip: “Weekend visitors? Test this before checkout.”
  • CTA: “Need help? Tap ‘Call’—same-day slots before 3pm.”
  • ALT-TEXT (slide 2): “Close-up of a dripping faucet with $ sign overlay; text: ‘Silent leaks = pricey bills.’”

Example B — Kingman boutique (TikTok 15s)

  • Hook (on-screen): “Local gift ideas under $25 (Kingman).”
  • B-roll: 5 quick cuts of items + price tags + a “gift wrap” shot.
  • CTA (pinned comment): “DM ‘GIFT’ for curbside pickup hours.”

Advanced: non-generic prompts that force depth

These patterns use best practices: give role + goal, supply examples, set constraints/format, then ask for a self-check.

FAQs

Quick answers to common “Can ChatGPT do that?” questions.

Can I make ChatGPT use my real data?
Yes—paste your captions, analytics snapshots, or review snippets. It will analyze patterns and suggest better hooks/topics. Don’t paste secrets; anonymize customer info.
How do I write good ALT text for social images?
Be concise and describe what matters to understanding the post (not “image of”). See W3C tutorials and X/Twitter’s help.
How do I track which post drove the clicks?
Use UTM-tagged links (e.g., utm_source=instagram&utm_medium=social&utm_campaign=topic_202508) and check them in your analytics. Try Google’s Campaign URL Builder.
How do I avoid generic AI content?
Give it real inputs (your posts, reviews, event calendar), force constraints (tone, length, CTA), and require a self-critique step. See prompt-engineering best practices.