Synthetic data vs real data: when to use each
What each kind of data is good at, where synthetic data goes wrong, and how to test a model that uses both.
Guides
Guides for teams buying AI training data
Short, plain guides on the decisions that shape an AI training data project. Each one gives you the answer first, then the reasoning, written by the people who do the work. Six so far, more are on the way.
guide · synthetic vs real data
guide 1
guide · choosing a vendor
guide 2
the short answer, first
- Real data or synthetic?
- Which vendor to trust?
- What to ask before signing?
✓ Short answer first then the reasoning
6 Guides so far more on the way
The guides
Start with the decision in front of you
Six so far, each about one decision you'll face early in a project. Pick the one in front of you.
How to choose a data annotation vendor: the questions worth asking
The questions worth asking a vendor, what good answers sound like, and how we answer them ourselves.
Speech dataset specification: what to put in your collection brief
Scripted or spontaneous speech, who should be speaking, recording conditions and metadata, plus a brief template you can copy.
What makes data annotation quality good, or bad
Guideline clarity, inter-annotator agreement, how edge cases get resolved, and the review layers that catch mistakes.
Transcription for AI training data, versus for people to read
Verbatim vs clean text, timestamps, speaker labels, and how to decide which one your project needs.
How to plan an egocentric video data collection
The decisions behind a first-person video dataset, from device and mount to privacy, labels and checks, with a brief template to copy.
How these guides work
How each guide is laid out
The guides describe how the field works in general. Where a guide describes how we work, it says so in plain terms. For single words rather than whole decisions, see the AI training data glossary.
The answer up top
Each guide opens with a short answer you can use straight away, even if you read nothing else.
Then the reasoning
Why that answer holds, where it doesn't, and the figures that make the trade-off easy to see.
Questions at the end
The ones people ask most, answered in a sentence or two. Our own practice sits in marked "How we answer" boxes.
Also worth reading
Pages that explain the data itself
Not guides, but they answer the "what is it" questions in the same plain way.
Speech data
What a speech dataset carries, clip by clip.
Image data
Photos, labels and what to specify.
Video data
Frames, tracks and events.
Text data
Text written, labeled and checked.
Question the guides didn't answer?
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