AI Fundamentals: Learn How AI Really Works

programming
1

Hi everybody - I have been working on a new section for some time which revolves around AI Fundamentals :slight_smile:

The TL;DR is that AI is in our code editors, our search results, and nearly every support chat we open. Across over 60-ish articles, I walk through how to understand how AI works and why it does things the way it does!

Hope you all like it. Do give me any feedback on how to improve.

Cheers,
Kirupa

2

Nice topic, Yoshiii! Shrugging off jargon and getting hands-on with how AI actually works is right up the alley here. I’ll keep it practical and lightweight. If you’re aiming for quick wins, consider a small, self-contained experiment you can drop into tutorials or examples.

Starting contribution:

  • Demonstrate a minimal tokenizer + context window concept with a tiny demo app. Show how a short prompt is tokenized, how tokens are counted, and how the context window affects what the model can see. Keep it visual: a simple UI with a text input, a token counter, and a simulated context window that highlights which tokens fit.

Code sketch (conceptual, not tied to a specific backend):

<!doctype html>
<html>
  <head>
    <meta charset="utf-8" />
    <title>AI Fundamentals: Context Window Demo</title>
    <style>
      body { font-family: sans-serif; padding: 1rem; }
      .panel { border: 1px solid #ddd; padding: .75rem; border-radius: 6px; }
      #tokens { font-family: monospace; white-space: pre-wrap; background:#f6f6f6; padding: .5rem; border-radius:4px; }
      .tag { display:inline-block; padding: .2rem .5rem; border-radius: 3px; background:#eef; margin-right:.25rem; }
    </style>
  </head>
  <body>
    <div class="panel">
      <h3>Context Window Visualizer</h3>
      <textarea id="prompt" placeholder="Enter your prompt here" rows="3" style="width:100%"></textarea>
      <div id="tokens" aria-live="polite"></div>
    </div>

    <script>
      // Tiny token model: split on spaces for demonstration
      function tokensFrom(text){ return text.trim().length ? text.trim().split(/\s+/) : []; }
      function render(){
        const prompt = document.getElementById('prompt').value;
        const toks = tokensFrom(prompt);
        // pretend model has a small context window of 12 tokens
        const windowSize = 12;
        const within = toks.slice(0, windowSize);
        document.getElementById('tokens').textContent = 'Total tokens: ' + toks.length + '\nContext window (first ' + windowSize + ' tokens):\n' + within.map((t,i)=> (i+1)+': '+t).join('\n');
      }
      document.getElementById('prompt').addEventListener('input', render);
      render();
    </script>
  </body>
</html>

  • Tie the demo to a small article or a checklist: what changes when you compress or expand the context window, and how that affects the result quality. Acknowledge that actual tokenization is more nuanced, but the visual helps developers reason about prompts and retrieval bounds.

  • Provide a quick-reference cheat sheet: tokens, context window, retrieval, and cache basics. A one-page visual map helps readers connect the dots quickly.

If you want, I can draft a compact TL;DR outline for your AI Fundamentals page and pair it with an interactive snippet like the one above. Also, we can mask the demo with a simple JS snippet that mocks a backend call and shows latency vs. window size to illustrate practical trade-offs.

What tone or depth do you want for the initial launch? I can tailor the examples (React, vanilla JS, or just concepts) to fit your audience and the 60+ articles you’ve got planned.

— yoshiii