ai-clustering

---

name: ai-clustering description: Cluster/categorize a large dataset using AI with a two-round map-reduce approach: batch items to generate candidate category labels, reduce to a unified set, then categorize every item. Use when you need to discover and apply categories to a large corpus of text, prompts, products, tickets, or any unstructured data.

AI Clustering (Map-Reduce Pattern)

Two rounds. Categories emerge from the data — never hardcode them upfront.

Round 1 — Map + Reduce

Map: Feed batches of items into concurrent AI calls. Each call outputs candidate category labels describing patterns it sees.

Reduce: One AI call merges all candidate labels into a clean, deduplicated unified set.

Round 2 — Categorize

Feed all items through batched concurrent AI calls using the unified category set. Each item gets assigned one (or N) category labels.

---

Implementation Pattern

// Round 1 Map — per batch
async function r1Batch(items: Item[], idx: number): Promise<string[]> {
  const block = items.map(item => `- ${snippet(item)}`).join('\n')
  const { output } = await generateText({
    model: gateway('google/gemini-2.5-flash-lite'),
    output: Output.object({ schema: z.object({ categories: z.array(z.string()) }) }),
    temperature: 0.3,
    prompt: `[CONTEXT: explain what these items ARE and what dimension to cluster on]
Study these ${items.length} items. Generate category labels describing [HOW/WHAT/WHICH dimension].
Items: ${block}
Return 5–12 labels in lowercase-kebab-case.`,
  })
  if (!output) throw new Error('AI failed to generate categories')
  return output.categories
}

// Round 1 Reduce
async function reduce(allCandidates: string[][]): Promise<Category[]> {
  const unique = [...new Set(allCandidates.flat())]
  const { output } = await generateText({
    model: gateway('google/gemini-2.5-flash-lite'),
    output: Output.object({ schema: z.object({ categories: z.array(z.object({ label: z.string(), description: z.string() })) }) }),
    temperature: 0.1,
    prompt: `Merge these raw labels into a clean unified set. Merge synonyms.
Labels: ${unique.join(', ')}
Target 8–15 final categories with a one-sentence description each.`,
  })
  if (!output) throw new Error('AI failed to reduce categories')
  return output.categories
}

// Round 2 Categorize — per batch
async function r2Batch(items: Item[], categories: Category[], idx: number): Promise<Map<string, string>> {
  const catList = categories.map(c => `- ${c.label}: ${c.description}`).join('\n')
  const block = items.map(i => `${i.id}|||${snippet(i)}`).join('\n')
  const { output } = await generateText({
    model: gateway('google/gemini-2.5-flash-lite'),
    output: Output.object({ schema: z.object({ results: z.array(z.object({ id: z.string(), category: z.string() })) }) }),
    temperature: 0,
    prompt: `CATEGORIES:\n${catList}\n\nAssign each item to exactly ONE category.\n${block}`,
  })
  if (!output) throw new Error('AI failed to categorize items')
  const map = new Map<string, string>()
  for (const r of output.results) map.set(r.id, r.category)
  return map
}

---

Critical Rules

Context is everything

Tell the AI what the items are and which dimension to cluster on. Without this, it defaults to the most obvious semantic grouping (which is usually wrong).

❌ "Classify these prompts"
✅ "These are ALL initial project creation prompts typed into an AI app-builder.
    Every one is trying to build an app — that's a given.
    Cluster by HOW the user wrote it (format, structure, communication style), not WHAT they want to build."

Snippet + metadata — never raw truncation

When truncating long text, append length metadata so the model can factor in the full scope:

function snippet(item: Item, maxChars = 200): string {
  const clean = item.text.replace(/\r?\n/g, ' ').trim()
  const words = clean.split(/\s+/).length
  const snip  = clean.slice(0, maxChars)
  return clean.length > maxChars
    ? `${snip} [TRUNCATED — full: ${words}w ${clean.length}ch]`
    : snip
}

Throttled concurrency + timeout + resume

const CONCURRENCY = 12
const TIMEOUT_MS  = 90_000

async function withTimeout<T>(p: Promise<T>, ms: number): Promise<T> {
  return Promise.race([p, new Promise<never>((_, r) => setTimeout(() => r(new Error('timeout')), ms))])
}

// Save progress after every batch — reruns pick up where they left off
assignments = fs.existsSync(CACHE) ? JSON.parse(fs.readFileSync(CACHE, 'utf-8')) : {}
const pending = batches.filter(b => b.some(item => !assignments[item.id]))

Batch sizing guidelines

Dataset size	Batch size	# batches
~100 items	30	~4
~1000 items	80–100	~12
~10000 items	100	~100

Use gemini-2.5-flash-lite for both rounds — fast, cheap, good at classification.

---

What emerges vs what to hardcode

Let emerge from data (Round 1): The actual category labels and their meanings.

Hardcode only: The clustering dimension — i.e. the question you're asking the data.

Examples of good dimensions:

• "HOW the prompt is written (format/style)" — not "what app they want"
• "Industry vertical" — not "company size"
• "Root cause" — not "which team owns it"

---

Sampling after clustering

Pick representative items from each cluster spanning the full length distribution:

function pickSamples(items: Item[], n: number): Item[] {
  const sorted = [...items].sort((a, b) => a.text.length - b.text.length)
  const third  = Math.floor(sorted.length / 3)
  const pick   = (g: Item[], k: number) => {
    const med = g[Math.floor(g.length / 2)]?.text.length ?? 0
    return [...g].sort((a, b) => Math.abs(a.text.length - med) - Math.abs(b.text.length - med)).slice(0, k)
  }
  const perT = Math.ceil(n / 3)
  return [
    ...pick(sorted.slice(0, third), perT),
    ...pick(sorted.slice(third, third * 2), perT),
    ...pick(sorted.slice(third * 2), n - perT * 2),
  ].slice(0, n)
}

This gives you short + medium + long representatives from each cluster — not just the average.