About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

RAG & NLP • Browser • 20-30 min

Lite Parse

LiteParse is a fast, model-free PDF parsing library that converts PDFs to markdown. It uses:.

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Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

LiteParse is a fast, model-free PDF parsing library that converts PDFs to markdown. It uses:.

Learning objectives

  • Explain the main rag/nlp decision that Lite Parse is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for parsed structure, extracted fields, and parsing failures
  • Observe how format variation affects extraction reliability

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.