Side-by-side eval

Side-by-side eval presents two model or prompt outputs to a rater (human or LLM judge) for direct comparison — "which one is better?" — instead of grading each on an absolute scale.

Side-by-side evals (also called pairwise comparison) often produce more reliable judgments than absolute scoring because raters anchor on the comparison rather than an arbitrary 1-5 scale. Used heavily in 2026 when promoting a new prompt or model: take the golden set, run both, present pairs to LLM judges + a human sample, pick the winner per pair, aggregate. Tools: Braintrust, LangSmith pairwise evals, custom Streamlit dashboards. Best practice: randomise order to avoid position bias, run multiple LLM judges to reduce single-judge bias.

When to use side-by-side eval

• Promoting a new prompt or model variant.
• Choosing between close alternatives where absolute scores hard to calibrate.

Common mistakes

• Not randomising order — judges have position bias.
• Single LLM judge — calibrate with a jury or human spot-check.

FAQ

Related terms

• Evals (LLM evaluations) — Evals are systematic tests that measure how well a language model or LLM-powered system performs on a defined task using a golden set of inputs and reference outputs.
• A/B testing prompts — A/B testing prompts runs two prompt variants against the same input distribution and compares scored outputs, attributing quality differences to the prompt change.
• LLM jury — An LLM jury is an evaluation pattern where multiple LLM judges score the same output, and their scores are aggregated to reduce single-judge bias.
• Vibe eval — Vibe eval is the pejorative for unsystematic eyeball-grading of LLM output — "it feels better" rather than measurable rubric-based comparison. The opposite of proper evals.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/side-by-side-eval.md.