Poly-Glot Markdown

🏷️ Frontmatter 📐 Structure 🔗 Semantic 🎯 GEO ✂️ RAG Chunking 🧩 MDX

Format Markdown and MDX files for better RAG retrieval, GEO discoverability, and AI comprehension.

⚡ Instant Mode

7 rules in your browser — zero API calls

🏷️ Frontmatter 📐 Heading Hierarchy 🔗 Citation Anchors 🎯 GEO Keywords ✂️ RAG Chunking 📝 Summaries 🔑 Keyword Extraction Engine

🧠 AI-Enhanced Mode

Deep semantic rewriting via Gemini

Rewrites for clarity, generates natural summaries, context-aware GEO optimization, intelligent restructuring — all powered by Gemini 3.6 Flash.

FREE — no API key needed

🤖 AI Powered — Instant Mode applies client-side rules; AI-Enhanced Mode uses Gemini 3.6 Flash for semantic rewriting (free, no API key needed)
📚 Searchable — better RAG retrieval
🌐 Discoverable — GEO optimized for LLM citation
🔒 Private — Instant Mode runs entirely in your browser. AI-Enhanced Mode sends your text to our server (never stored, never logged)

🔍 Why RAG & GEO Matter

📚

RAG — Retrieval-Augmented Generation

RAG is how AI systems find and use your content. When someone asks ChatGPT, Copilot, or an internal AI assistant a question, RAG is the pipeline that retrieves relevant documents, chunks them, and feeds them to the language model.

The problem: Most documentation is written for humans, not machines. AI retrieval systems split documents into chunks — and poorly structured docs produce bad chunks. Missing frontmatter means no metadata filtering. No heading hierarchy means chunks split mid-thought. No summaries means the AI has to guess what each section is about.

What Poly-Glot fixes:

Adds YAML frontmatter so vector databases can filter by title, tags, and date
Enforces heading hierarchy (H1 → H2 → H3) so chunkers split at semantic boundaries
Inserts section summaries so the most important info appears at the top of each chunk — where LLMs pay the most attention (Liu et al. 2023)
Adds explicit chunk boundaries () to prevent mid-sentence splits
Generates citation anchors ({#section-slug}) for passage-level retrieval

↑ 40% Retrieval accuracy improvement from structured chunking vs. naive splitting (Pinecone, 2023)

🌐

GEO — Generative Engine Optimization

GEO is the new SEO. When users search with AI engines like Perplexity, Google AI Overviews, ChatGPT Search, or Gemini — those engines cite sources. GEO determines whether your content gets cited or someone else's.

The problem: Traditional SEO optimizes for link ranking. But AI search engines don't show ten blue links — they generate answers and cite sources inline. If your content lacks authoritative signals (clear titles, bolded key phrases, structured data, freshness dates), AI engines will cite your competitors instead.

What Poly-Glot fixes:

Generates descriptive titles and meta descriptions that AI engines use for summarization
Bolds 3–5 key phrases per section — the exact pattern that increases AI citation likelihood (Aggarwal et al. 2023)
Extracts and surfaces topical keywords/tags for semantic matching
Adds author attribution and freshness dates (last_reviewed, expires) for trust scoring
Structures content with clear Q&A patterns that AI engines prefer to cite

↑ 20–40% Visibility improvement from keyword optimization and authoritative language (GEO paper, Princeton/Georgia Tech 2023)

❌ Without Optimization

AI retrieval returns wrong chunks
Perplexity/ChatGPT cite competitors
Internal AI tools hallucinate from poor context
Vector search returns low-relevance results
Documentation gets stale with no freshness signals

✅ With Poly-Glot Markdown

Every chunk has context, metadata, and summaries
AI search engines cite your content by name
Internal RAG pipelines return precise answers
Vector embeddings capture true document meaning
Freshness metadata keeps content current and trusted

How It Works

1Paste or upload your Markdown / MDX

→

2Choose your optimization goals

→

3Get RAG & GEO optimized output instantly

What Gets Optimized

🏷️

FrontmatterAdds or enriches YAML frontmatter with title, description, tags, and author

📐

StructureEnforces clear heading hierarchy (H1 → H2 → H3) for semantic chunking

🔗

Semantic ClarityRewrites ambiguous sentences so AI models understand intent

🎯

GEO KeywordsSurfaces and emphasizes key terms for generative engine discoverability

✂️

RAG ChunkingAdds section breaks and summaries optimized for vector database chunking

🧩

MDX ComponentsPreserves and annotates JSX components in MDX files without breaking them

What is MDX?

MDX = Markdown + JSX. Think of it as Markdown that can include interactive React components.

Markdown →

Headings, paragraphs, lists Links, code blocks

JSX / React →

Tabs, cards, buttons, callouts Interactive examples, API components Embedded demos

🎬 See It In Action

Watch how Poly-Glot Markdown transforms raw, unstructured docs into RAG-ready, GEO-optimized Markdown

Step 1

Before: Raw Markdown

❌ No frontmatter

❌ Poor structure

❌ Not RAG-ready

❌ No GEO keywords

→

Step 2

After: Optimized Output

✅ Frontmatter added

✅ Semantic structure

✅ RAG chunks

✅ GEO optimized

⚡

Instant Mode — What It Does

Seven deterministic rules run entirely in your browser with zero latency. No network calls, no API keys, works offline. Every transformation is predictable and repeatable.

🏷️ Frontmatter Generation Detects missing YAML frontmatter and generates it from document content — title extracted from H1, description from first paragraph, tags from keyword frequency analysis, plus last_reviewed, expires (90 days), source, and version fields. If frontmatter exists, enriches it with any missing fields without overwriting existing values.
📐 Heading Hierarchy Fix Enforces single-H1 rule — demotes duplicate H1s to H2. Scans for heading-level jumps (H1 → H3) that break semantic chunking in vector databases. Ensures clean H1 → H2 → H3 progression that tools like LlamaIndex and LangChain rely on for recursive splitting.
🔗 Citation Anchor IDs Adds {#section-slug} anchor IDs to every H2 and H3 heading. Enables passage-level retrieval — RAG systems can link directly to a specific section instead of returning the entire document. Slugs are auto-generated from heading text (lowercase, hyphenated).
🎯 GEO Keyword Bolding Extracts the top keywords using word frequency + bigram analysis with stopword filtering. Bolds up to 5 high-value phrases using **keyword** syntax. Skips headings, code blocks, and frontmatter. This is the single highest-impact GEO optimization — bolded key phrases increase AI search citation by 20–40% (Aggarwal et al. 2023).
✂️ RAG Chunk Boundaries Inserts  markers between major sections (before each H2). Prevents vector database chunkers from splitting mid-paragraph or mid-thought. Explicit boundaries give you control over where chunks break instead of relying on token-count splitting.
📝 Section Summaries Generates a > **Summary:** ... blockquote after each H2 heading, extracted from the first 200 characters of section content. Places the most important information at the top of each chunk — critical because LLMs attend most to the beginning of retrieved passages (Liu et al. "Lost in the Middle" 2023).
🔑 Keyword Extraction Engine Strips frontmatter and code blocks, tokenizes text, filters 80+ English stopwords, counts unigram and bigram frequencies (bigrams boosted 1.5×), and returns the top 10 terms. Used by both frontmatter tag generation and GEO bolding. Entirely client-side — your content never leaves the browser.

🧠

AI-Enhanced Mode — What It Does

Sends your document to Google Gemini 3.6 Flash via the Poly-Glot server for deep semantic rewriting. The AI understands your content's meaning — not just its structure — and produces naturally written, publication-ready output.

🧠 Semantic Rewriting Gemini reads and understands your entire document, then rewrites vague or ambiguous sentences for clarity. Converts run-on paragraphs into structured, scannable text. Preserves your voice and tone while making every sentence precise enough for AI retrieval.
🏷️ Intelligent Frontmatter Generates contextually accurate metadata — the AI writes a real description (not just the first sentence), selects semantically relevant tags, and adds freshness fields (last_reviewed, expires, source, version). Enriches existing frontmatter without overwriting.
📝 Natural Summaries Instead of extracting the first 200 characters, the AI writes a genuine summary for each H2 section that captures the key takeaway — the kind of summary a human editor would write. These summaries become the most-attended text in each RAG chunk.
🎯 Context-Aware GEO Optimization The AI identifies which phrases actually matter for search visibility based on semantic understanding, not just word frequency. Bolds the phrases that AI search engines (Perplexity, ChatGPT, Google AI Overviews) are most likely to cite.
📐 Intelligent Restructuring Fixes heading hierarchy, splits oversized sections (>500 words) into logical sub-sections, merges tiny fragments, and adds anchor IDs — all while maintaining logical flow. The AI understands where a new section should start based on topic shifts, not just word count.
✂️ Smart Chunk Optimization Places chunk boundaries at natural topic transitions. Adds summary blockquotes and citation anchors. Targets 100–500 words per chunk — the sweet spot for embedding models like text-embedding-3-small and voyage-3.
🧩 MDX Preservation When MDX mode is enabled, the AI preserves all JSX components (<Callout>, <Tab>, <CodeBlock>) and adds descriptive comments above each component explaining its purpose — making MDX files both human-readable and AI-parseable.

✨ Markdown Formatter

Paste or upload your Markdown below. Choose optimization options and click Format. When done, click 🔬 → Pipeline in the output panel to send your formatted doc straight to the RAG Pipeline for embedding, search, and AI-powered Q&A.

⚡ Instant Mode Client-side rules — instant, works offline

Input

📁 Upload File

Optimize for: Frontmatter Structure Semantic Clarity GEO Keywords RAG Chunking MDX Components

→

Optimized Output

✨

Your optimized Markdown will appear here

RAG-ready · GEO-optimized · Structured

🔬 In-Browser RAG Pipeline

A complete retrieval-augmented generation pipeline running entirely in your browser. Format your docs in the Format tab, then embed, search, and query them here — no backend, no signup, no cost.

Transformers.js all-MiniLM-L6-v2 IndexedDB Cosine Similarity Gemini 3.6 Flash

📋 How to Use

⬇️

STEP 1

Load Model

One-time download
~23 MB, cached

🧮

STEP 2

Embed Docs

Paste or upload
markdown files

🔍

STEP 3

Semantic search
across your docs

💬

STEP 4

Ask

AI answers from
your documents

💡 Tip: Format your docs in the 📝 Format tab first for best results. Formatted docs produce better chunks, richer metadata, and more accurate retrieval.

🎥 Full Walkthrough: Format → RAG Pipeline

Watch the complete flow — format a document with RAG/GEO rules, then chunk, embed, search, and get AI-grounded answers.

🖥️ This Demo (In-Browser)

Embeddings	Transformers.js — all-MiniLM-L6-v2, 384-dim, WASM
Vector Store	IndexedDB — browser-native, zero setup
Search	Cosine Similarity — brute-force, <10K chunks
LLM	Gemini 2.5 Flash — free tier, grounded answers
Chunking	H2 headings + `<!-- chunk-boundary -->`

🚀 Production Scale-Up Path

Embeddings	OpenAI text-embedding-3-small / Cohere v3
Vector DB	Pinecone · Qdrant · Weaviate
Search	HNSW indexing — sub-10ms at 100K+
LLM	GPT-4o · Claude · Gemini Pro
Orchestration	LangChain · LlamaIndex

Why IndexedDB? Zero-dependency, runs offline, full RAG loop without infrastructure. In production, swap for Pinecone or Qdrant — chunking and embedding logic stays identical. Same pattern used by Arize Phoenix for retrieval evaluation.

Poly-Glot Markdown full walkthrough — Format tab formats a document, then Pipeline tab loads model, embeds chunks, searches, and generates AI answers

Captured from the live UI · Loops automatically

Load Embedding Model

~23 MB · runs in WebAssembly

Downloads all-MiniLM-L6-v2 via Transformers.js. Runs locally in your browser — your content never leaves your machine for embeddings.

Not loaded

Embed Document

chunk → vectorize → store

Paste formatted Markdown (or use your Format tab output). The document is chunked by H2 sections, each chunk is embedded into a 384-dim vector, and stored in IndexedDB.

📁 Upload .md

Semantic Search

cosine similarity · top-5

Type a query. It gets embedded with the same model, then compared against all stored chunks using cosine similarity. Returns the top 5 most relevant passages.

Ask (RAG)

retrieve → Gemini → grounded answer

Ask a natural language question. The top-5 chunks are retrieved and sent to Gemini 3.6 Flash as context. The LLM generates an answer grounded in your documents — with source citations.

⚡ Instant Mode

🧠 AI-Enhanced Mode

🔍 Why RAG & GEO Matter

RAG — Retrieval-Augmented Generation

GEO — Generative Engine Optimization

❌ Without Optimization

✅ With Poly-Glot Markdown

How It Works

What Gets Optimized

What is MDX?

🎬 See It In Action

Before: Raw Markdown

After: Optimized Output

Instant Mode — What It Does

AI-Enhanced Mode — What It Does

✨ Markdown Formatter

🔬 In-Browser RAG Pipeline

📋 How to Use

🎥 Full Walkthrough: Format → RAG Pipeline

Load Embedding Model

Embed Document

Semantic Search

Ask (RAG)

📐 How Scoring Works

RAG Score (0–100)

GEO Score (0–100)

📚 References

Poly-Glot Markdown

⚡ Instant Mode

🧠 AI-Enhanced Mode

🔍 Why RAG & GEO Matter

RAG — Retrieval-Augmented Generation

GEO — Generative Engine Optimization

❌ Without Optimization

✅ With Poly-Glot Markdown

How It Works

What Gets Optimized

What is MDX?

🎬 See It In Action

Before: Raw Markdown

After: Optimized Output

Instant Mode — What It Does

AI-Enhanced Mode — What It Does

✨ Markdown Formatter

Diff View

🧩 Chunk Preview

🔬 In-Browser RAG Pipeline

📋 How to Use

🎥 Full Walkthrough: Format → RAG Pipeline

Load Embedding Model

Embed Document

Semantic Search

Ask (RAG)

📐 How Scoring Works

RAG Score (0–100)

GEO Score (0–100)

📚 References