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Golden datasets and synthetic eval

Hand-curated Q&A pairs + LLM-generated test sets — and how to tell them apart.

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Overview

Hand-curated Q&A pairs + LLM-generated test sets — and how to tell them apart.

Why it matters

A golden set is the ground truth your CI runs against. Build it once, version it like code, and every model change becomes measurable.

Where this sits in the stack

Golden datasets and synthetic eval is one of the load-bearing decisions in a KG/RAG/agent system: choices made here propagate to retrieval quality, agent reliability, cost per query, and the on-call burden of whoever ships it. Teams that name this trade-off explicitly ship faster than teams that leave it implicit.

Why this is load-bearing

Golden datasets and synthetic eval is the building-code of this layer. You can ignore building codes on a shed, but the moment you put two storeys on top of the same foundation they decide whether the structure stands or falls. In a KG/RAG/agent stack, the equivalent of 'two storeys' is the second feature you ship on top of this primitive — GraphRAG on top of chunking, supervisor agents on top of state machines, regression CI on top of metrics. The cost of cutting the wrong corner now is paid by every later layer, with interest.

A golden set is the ground truth your CI runs against. Build it once, version it like code, and every model change becomes measurable.

Reflect — apply it

Anchor golden datasets and synthetic eval to something concrete in your own work.

  • ›Where have you seen golden datasets and synthetic eval done well? Name one team or product and what they got right.
  • ›Where have you seen it done badly? What was the first symptom that surfaced (latency, hallucination, cost, outage)?
  • ›What is the *cheapest* version of this you could ship in your next sprint, and what single metric would tell you it's working?

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