[G1-Sync] Manual knowledge update
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Antigravity Agent
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---
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id: ai-synthetic-data
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title: Synthetic Data — LLM 으로 train / test / fixture
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category: Coding
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status: draft
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source_trust_level: B
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verification_status: conceptual
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created_at: 2026-05-09
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updated_at: 2026-05-09
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tags: [ai, synthetic-data, vibe-coding]
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tech_stack: { language: "TS / Python", applicable_to: ["Backend"] }
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applied_in: []
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aliases: [synthetic data, LLM-generated data, test fixtures, data augmentation, anonymization]
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---
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# Synthetic Data
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> LLM 가 fake data 생성. **Test fixture, ML training, 사용자 demo, anonymization**. Real data privacy / cost / scale 우회.
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## 📖 핵심 개념
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- Generation: LLM 가 schema 따라 data 생성.
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- Augmentation: 기존 data 의 변형.
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- Anonymization: PII 제거 + realistic 유지.
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- Distillation: 큰 model → 작은 model 의 training.
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## 💻 코드 패턴
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### LLM 으로 fixture 생성
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```ts
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import { z } from 'zod';
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import OpenAI from 'openai';
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import { zodResponseFormat } from 'openai/helpers/zod';
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const User = z.object({
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email: z.string().email(),
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name: z.string(),
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bio: z.string().max(200),
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interests: z.array(z.string()).max(5),
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age: z.number().int().min(18).max(80),
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});
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async function generateUsers(count: number): Promise<z.infer<typeof User>[]> {
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const r = await openai.beta.chat.completions.parse({
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model: 'gpt-4o-mini',
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messages: [
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{ role: 'system', content: 'Generate diverse, realistic test user profiles. Vary demographics, names, bios.' },
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{ role: 'user', content: `Generate ${count} users.` },
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],
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response_format: zodResponseFormat(z.object({ users: z.array(User) }), 'users'),
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});
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return r.choices[0].message.parsed!.users;
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}
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const users = await generateUsers(50);
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```
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→ Faker.js 보다 realistic.
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### Diverse generation
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```ts
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// 단순 — 비슷한 데이터 자주
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// Better — diversity prompt
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const prompts = [
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'Generate users from different countries',
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'Generate users with different age groups',
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'Generate users with different income levels',
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];
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const all: User[] = [];
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for (const prompt of prompts) {
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const batch = await generateWithPrompt(prompt, 20);
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all.push(...batch);
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}
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```
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### Schema-driven (any)
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```ts
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const Order = z.object({
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id: z.string().uuid(),
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userId: z.string().uuid(),
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items: z.array(z.object({
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productId: z.string().uuid(),
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quantity: z.number().int().positive(),
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price: z.number().positive(),
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})).min(1).max(10),
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status: z.enum(['pending', 'paid', 'shipped', 'delivered', 'cancelled']),
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createdAt: z.string().datetime(),
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});
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const orders = await generateFromSchema(Order, 100);
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```
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### Faker.js (deterministic, fast)
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```ts
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import { faker } from '@faker-js/faker';
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faker.seed(42); // deterministic
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const user = {
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id: faker.string.uuid(),
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name: faker.person.fullName(),
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email: faker.internet.email(),
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address: {
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street: faker.location.streetAddress(),
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city: faker.location.city(),
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zip: faker.location.zipCode(),
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},
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};
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```
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→ 빠름, 일관, but 패턴 명확 (LLM 보다 less realistic).
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### Hybrid (Faker + LLM)
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```ts
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// Faker = structure (id, email, address)
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// LLM = creative (bio, review text)
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const user = {
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id: faker.string.uuid(),
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email: faker.internet.email(),
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bio: await llm.generate('Write a 100-character bio for a freelance designer'),
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reviews: await llm.generate('Write 3 realistic product reviews'),
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};
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```
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### Test database seed
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```ts
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async function seed() {
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await db.user.deleteMany();
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await db.order.deleteMany();
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const users = await generateUsers(100);
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await db.user.createMany({ data: users });
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const orders = await generateOrders(500, users.map(u => u.id));
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await db.order.createMany({ data: orders });
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console.log(`Seeded ${users.length} users, ${orders.length} orders`);
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}
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```
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```bash
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yarn seed
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```
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→ Test environment 가 production-like.
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### Anonymization (real → synthetic)
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```ts
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// Real user data → similar but anonymized
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async function anonymize(user: User): Promise<User> {
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const r = await llm.complete({
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system: 'Generate a realistic user profile similar to this one but with all PII changed.',
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user: `Original: ${JSON.stringify(user)}`,
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response_format: { type: 'json_object' },
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});
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return JSON.parse(r);
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}
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// Or simpler — Faker
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function anonymize(user: User): User {
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return {
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...user,
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name: faker.person.fullName(),
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email: faker.internet.email(),
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phone: faker.phone.number(),
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// 비-PII keep (purchase history, preferences)
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};
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}
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```
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→ Test on prod-like data without exposure.
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### ML training data augmentation
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```ts
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// Few-shot examples → 더 많은 generation
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async function augmentDataset(examples: Example[], targetSize: number) {
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const augmented: Example[] = [...examples];
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while (augmented.length < targetSize) {
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const batch = await llm.generate({
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system: 'Generate similar examples to these, with variations.',
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user: examples.slice(0, 5).map(e => JSON.stringify(e)).join('\n'),
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response_format: { type: 'json_object' },
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});
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augmented.push(...JSON.parse(batch).examples);
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}
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return augmented.slice(0, targetSize);
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}
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```
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→ 100 examples → 1000.
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### Distillation (big → small model)
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```ts
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// 1. Big model (GPT-4o) 가 답 생성
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// 2. (input, output) 쌍 = training data
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// 3. Small model (Llama 8B) fine-tune
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async function generateTrainingData(inputs: string[]) {
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const data = [];
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for (const input of inputs) {
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const output = await openai.chat.completions.create({
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model: 'gpt-4o',
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messages: [{ role: 'user', content: input }],
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});
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data.push({ input, output: output.choices[0].message.content });
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}
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return data;
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}
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// 그 후 fine-tune small model.
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```
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→ Cost ↓ runtime, 비슷 quality.
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### Edge case generation
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```ts
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async function generateEdgeCases(schema: any, count: number) {
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return await llm.generate({
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system: `Generate edge case test inputs based on this schema.
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Include: empty, very long, special chars, boundary values, unicode, malformed.`,
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user: JSON.stringify(schema),
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response_format: { type: 'json_object' },
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});
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}
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```
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### Adversarial (security test)
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```ts
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async function generateAdversarial(target: string, count: number) {
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return await llm.generate({
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system: `Generate adversarial inputs for security testing.
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Include: SQL injection attempts, XSS, command injection, long strings, unicode tricks.`,
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user: `Target: ${target}`,
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});
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}
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```
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→ Pen testing.
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### Validation (synthetic 가 real 같은가?)
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```ts
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// Statistical check
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const realStats = computeStats(realData);
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const synthStats = computeStats(syntheticData);
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// Distribution similarity (KS test, etc)
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expect(ksDistance(realStats, synthStats)).toBeLessThan(0.1);
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```
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### Privacy guarantee
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```
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GDPR / HIPAA:
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- Synthetic data 가 individual 추적 불가
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- Differential privacy 가 강한 보장
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Tools:
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- gretel.ai
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- Mostly AI
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- YData
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```
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### Use cases
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```
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✅ Test fixtures (unit / integration / e2e)
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✅ Demo / sandbox
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✅ Load test data
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✅ ML training augmentation
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✅ Privacy-preserving sharing
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✅ Edge case generation
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✅ Adversarial testing
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❌ Production data 대체 (real distribution 다름)
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❌ Statistical analysis (bias)
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```
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### LLM-as-judge (synthetic 검증)
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```ts
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async function evaluateSynthetic(real: any[], synthetic: any[]) {
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return await llm.complete({
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user: `Compare these two datasets:
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Real: ${JSON.stringify(real.slice(0, 10))}
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Synthetic: ${JSON.stringify(synthetic.slice(0, 10))}
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Are they similar in style, distribution, realism? Score 1-10. Output JSON.`,
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response_format: { type: 'json_object' },
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});
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}
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```
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### Cost
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```
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1000 records × 100 tokens × $5/1M = $0.50
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→ Cheap.
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ML training data:
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10K records × 500 tokens × $5/1M = $25
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→ Still cheap vs human labeling.
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```
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### Reproducibility
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```ts
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// Seed
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const seed = 42;
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faker.seed(seed);
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// LLM = non-deterministic. Use temperature 0 + cache.
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const r = await openai.chat.completions.create({
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model: 'gpt-4o-mini',
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temperature: 0,
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seed: 42, // 일부 model
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messages: [...],
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});
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```
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### Volume
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```ts
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// 10K records — batch
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const BATCH = 50;
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const total = 10000;
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const all: any[] = [];
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for (let i = 0; i < total; i += BATCH) {
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const batch = await generate(BATCH);
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all.push(...batch);
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console.log(`${all.length}/${total}`);
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}
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```
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→ Rate limit / cost 주의.
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### Streaming (large dataset)
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```ts
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async function* generateStream(count: number) {
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for (let i = 0; i < count; i += 50) {
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const batch = await generate(Math.min(50, count - i));
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for (const item of batch) yield item;
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}
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}
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for await (const item of generateStream(10000)) {
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await db.insert(item);
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}
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```
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### Tools
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```
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- Mockaroo (web): schema → CSV/JSON
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- Faker.js / Faker (Python)
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- gretel.ai: privacy-preserving synthetic
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- SDV (Synthetic Data Vault): tabular ML
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- LLM (GPT-4o, Claude, local)
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```
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### Best practices
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```
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1. Schema first (Zod / Pydantic)
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2. Diverse prompts (variation)
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3. Validation 가 real distribution 비슷
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4. Privacy 검증 (no PII leak)
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5. Versioning (synthetic dataset 도)
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6. Cost monitoring
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```
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## 🤔 의사결정 기준
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| 사용 | 추천 |
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|---|---|
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| Unit test | Faker (deterministic) |
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| E2E test | Faker + LLM 조합 |
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| Demo / sandbox | LLM (realistic) |
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| ML training | LLM + augmentation |
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| Privacy 보존 | gretel / Mostly AI |
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| 큰 volume | Faker (cost) |
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## ❌ 안티패턴
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- **Real PII 변형 X — synthetic 가정**: privacy violation.
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- **모든 거 LLM (큰 cost)**: Faker 가 OK 자주.
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- **Distribution 가 real 같은 가정**: validate.
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- **Reproducibility 없음**: test flake.
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- **Seed 없음 (random)**: 다른 결과.
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- **Edge case 없음**: 일반 case 만 generate.
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- **Synthetic만 deploy production**: real 가 아님.
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## 🤖 LLM 활용 힌트
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- Schema-driven (Zod) + LLM = realistic.
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- Faker (cheap) + LLM (creative) hybrid.
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- Diverse prompt (multiple variation).
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- Privacy-aware (no PII generation).
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## 🔗 관련 문서
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- [[Testing_Faker_and_Builders]]
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- [[AI_Fine_Tuning_vs_Prompting]]
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- [[AI_LLM_Eval_Patterns]]
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