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Content Lab is Mudra’s workspace for turning tracked prompts into citation-ready pages. It takes the sources AI models already cite, adds fresh research, and ships a structured article that’s easy for models to quote. Content Lab generation steps

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What it is

  • Prompt-aligned: Every page starts from a tracked prompt (or cluster) you care about.
  • Source-aware: Uses the same domains/URLs AI models cite, not random web pages.
  • Structured for models: One clear H1, logical H2/H3s, short paragraphs, optional schema suggestions.
When you open Content Lab for a prompt, Mudra:
  1. Runs that prompt across supported AI models.
  2. Captures their answers and cited sources (domains + URLs).
  3. Uses those sources plus fresh research to build a better, more complete answer on your domain.
No diffing against your existing pages — the goal is a stronger, clearer version of what models already try to answer.

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How it works (behind the scenes)

  1. Ingest sources
    • Pulls the citations tied to the tracked prompt.
    • Filters and deduplicates; keeps up to 10; needs at least 2 to proceed.
  2. Scrape sources (Firecrawl)
    • Grabs the main content from each URL (markdown + links).
    • Keeps going even if some URLs fail; stops only if fewer than 2 succeed.
  3. Gap analysis
    • Compares scraped content against the tracked prompt.
    • Flags missing angles, data gaps, weak formats, and suggests search queries.
  4. Live research
    • Runs focused searches (recent data, original studies, expert quotes).
    • Adds a small set of additional sources with key insights.
  5. Draft generation
    • Produces a 1,200–1,600 word article with:
      • One H1 and direct-answer H2s
      • TL;DR right under the title
      • Short paragraphs (2–4 sentences, 50–75 words)
      • Comparison table when the prompt is comparative
      • Bottom Line + FAQ (3–5 Q&As)
    • Includes author name/title from the brand profile and tracks word count, sections, and sources.
Result: A complete, GEO-optimized article saved to your campaigns with all sources (scraped + research) logged.

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What you see in the UI

  • Step 1: Pick content type (Blog Post today; other formats next).
  • Step 2: Pick the tracked prompt (by category or list).
  • Step 3: Pick the target audience/ICP.
  • Step 4: Review & toggle citation sources (needs at least 2).
  • Step 5: Watch generation progress (validate → scrape → analyze gaps → research → draft).
  • Auto-redirects to the campaign page when ready.

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What it produces

  • Core article / pillar page — Direct answer to the tracked prompt with clear sections.
  • Supporting Q&As — FAQ blocks aligned to how users and models split the topic.
  • Research-enriched sections — Uses primary studies, reports, and fresh data instead of generic claims.
  • Schema-ready structure — Headings, FAQs, tables, and lists that are predictable for models.
  • Saved campaign — Article, metadata (title, sections, word count, author), and all sources tracked.

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How the agents help

  • Content Quality agent: Enforces clear titles, upfront answers, evidence-backed claims, and E-E-A-T signals.
  • Content Structure agent: Keeps the heading hierarchy clean, paragraphs short, and suggests tables/lists/FAQs.
  • Citation & Research agent: Spots claims that need proof, pulls recent high-authority sources, and keeps citations tidy.
You keep final voice and accuracy; agents handle the heavy lifting so the draft is safe to cite and easy to reuse.

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What to prepare before you start

  • The tracked prompt (or cluster) you want to win.
  • At least 2 citation sources tied to that prompt (auto-loaded if available).
  • Brand profile basics: brand name, description, target ICP, unique value prop, author name/title.
  • Optional: Any non-public data or examples you want woven in (you can paste them into the prompt context).

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Why it matters

  • You publish pages that match how models already answer — with better depth, fresher data, and clearer structure.
  • Every section is designed to be quotable and scannable by AI models.
  • Sources are tracked, research is fresh, and output is ready for GEO-focused distribution.