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This guide focuses on synthetic code data for production systems, with practical trade-offs for reliability, security, and cost.
Synthetic data is leverage only when the seed is yours. Generating from public GitHub clones competitors’ bugs, licenses, and style into your weights. Day 46 allowlists seeds and requires validators plus lineage.
⚡ TL;DR: Seed from internal ADRs, failing CI logs, and canonical SDK examples. Validate with compile/tests. Ban mass scrapes. Store lineage on every row.Allowed seeds: internal ADRs/RFCs; failing tests + redacted traces; platform-maintained SDK examples. Banned: mass GitHub scrape; random READMEs; customer PII without legal review.
python
adr = load("docs/adr/0032-auth.md")
prompt = f"Write an example using AuthClient per this ADR:\n{adr}"
sample = teacher.generate(prompt)
assert "AuthClient" in sample and compile_ok(sample)
seed_id, generator_model, validator, created_at — so bad synthetics can be purged.
synthetic code data: production guidance
- [ ] Seed allowlist documented
- [ ] Validators on code synthetics
- [ ] Lineage required
- [ ] Legal sign-off on any external seed
- [ ] Dedup pipeline
- [ ] Purge procedure tested
Series navigation
← Day 45 · Day 47 → Last updated September 11, 2026 Related: GitHub Copilot vs Cursor vs Claude Code vs Codeium: Which AI Coding Tool Wins in 2025; Cursor vs GitHub Copilot vs Claude Code: Which AI Coding Tool Wins in 2025; AI Code Review Automation: Build a GitHub Bot That Reviews PRs Like a Senior Engineer.Watch: 100 Days of AI on YouTube
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