Spent the last stretch building purely synthetic training data — no scraped text, no copyright gray zones, no messy dedup pipelines. Just generated data built to spec.

Some numbers from that work: 28 synthetic datasets shipped and published, plus an open-sourced 207M-parameter GPT foundation model trained end-to-end on that data (full portfolio: huggingface.co/CJJones).

What I learned building all of that:

• Synthetic > scraped for niche domains. If you're fine-tuning for legal, medical, or finance use cases, scraped web data is noisy and often legally risky. Generating from a tight schema gets you cleaner signal, faster.

• Schema-first beats prompt-first. Define the exact structure/fields you need before generating anything — it's the difference between a dataset that trains well and one that just looks good in a spreadsheet.

• Foundation model training is the best QA tool for your dataset. Training the 207M GPT on my own synthetic data surfaced dataset issues no amount of manual review would've caught (repetition patterns, distribution gaps, etc).

Now taking on custom synthetic dataset builds for AI/LLM teams — tell me your domain and spec, I generate production-ready data from scratch. If that's useful to you, my product page has the details.

---

💰 New: Affiliate Program (up to 10% commission)

Refer buyers to any plan (Starter $499 / Pro $1,499 / Enterprise $2,499) and earn a tiered commission — 5% to start, scaling to 7.5% and 10% as your referred sales grow each month. Check the affiliate page on our store to get your link.

I've been building custom synthetic datasets for teams fine-tuning niche LLMs, and the biggest thing I've noticed: most "bad" synthetic data isn't bad because the model that generated it was weak. It's bad because nobody specified the failure modes up front.

A few things that separate usable datasets from ones that quietly tank your fine-tune:

1. Distribution over volume. 50k rows that all look like the "happy path" is worse than 10k rows that cover edge cases, ambiguous inputs, and realistic noise. If your production traffic has typos, incomplete queries, or mixed intents, your training data needs those too — or your model will be brittle the moment it hits reality.

2. Schema discipline. Every row should map cleanly to the fields your app actually consumes downstream. I've seen teams generate gorgeous datasets that need a full re-parse before they're usable because nobody locked the schema before generation started.

3. Domain-specific failure cases, not generic ones. For legal/medical/finance use cases especially — the interesting (and risky) behavior is at the edges: ambiguous terminology, conflicting instructions, out-of-scope requests. Generic instruction-following data won't teach a model to handle that.

4. Multi-turn coherence, if your use case needs it. Single-turn synthetic examples are easy to generate and easy to get wrong for agentic or conversational products. If your model needs to hold context across turns, your training data needs multi-turn examples that actually test that, not just isolated Q&A pairs.

If you're evaluating synthetic data (from us or anyone), ask to see the edge-case distribution before you look at row count. Row count is a vanity metric. Coverage is the thing that determines whether your fine-tune actually generalizes.

Happy to answer questions on this if anyone's mid-fine-tune and hitting quality issues.

---

💰 New: Affiliate Program (up to 10% commission)

Refer buyers to any plan (Starter $499 / Pro $1,499 / Enterprise $2,499) and earn a tiered commission — 5% to start, scaling to 7.5% and 10% as your referred sales grow each month. Check the affiliate page on our store to get your link.