gbrain/docs/eval/METRIC_GLOSSARY.md

Evaluation Metric Glossary

Auto-generated from src/core/eval/metric-glossary.ts. Do not edit by hand. Run bun run scripts/generate-metric-glossary.ts to regenerate.

Every metric gbrain eval * and gbrain search stats reports has a plain-English explanation here. Industry terms are preserved verbatim so users searching the literature find what we report.

Retrieval Metrics

Precision at k (P@k)

Key: precision@k

Plain English: Of the top k results the engine returned, what fraction were actually relevant? High precision means few junk results in the top of the list.

Range: 0..1, higher is better. P@10 = 0.7 means 7 of the top 10 results were on-topic.

Recall at k (R@k)

Key: recall@k

Plain English: Of all the relevant results that exist in the brain, what fraction did the engine find in its top k? High recall means few missed answers.

Range: 0..1, higher is better. R@10 = 0.81 means out of every 100 questions, the right answer was in the top 10 for 81 of them.

Mean Reciprocal Rank (MRR)

Key: mrr

Plain English: On average, how far down the list is the FIRST relevant result? An MRR of 1.0 means the first hit is always right; an MRR of 0.5 means it's typically at rank 2.

Range: 0..1, higher is better. Computed as the average of 1/rank-of-first-relevant-result across all test queries.

Normalized Discounted Cumulative Gain at k (nDCG@k)

Key: ndcg@k

Plain English: Like precision@k, but the engine gets MORE credit for putting good results near the top than near rank k. A perfect ordering scores 1.0; a totally random ordering scores near 0.

Range: 0..1, higher is better. nDCG@10 above 0.65 is the common "ship it" threshold for hybrid retrieval on technical corpora.

Retrieval-Quality / Evidence Metrics (NamedThingBench)

Hit rate at 1 (Hit@1)

Key: hit@1

Plain English: Fraction of queries where the right page is the very first result. NamedThingBench hard-gates title-substring Hit@1 >= 0.95 and alias Hit@1 >= 0.98 — a query that is a page's name or title phrase should land it at rank 1, not "somewhere in the top 10".

Range: 0..1, higher is better.

Hit rate at 3 (Hit@3)

Key: hit@3

Plain English: Fraction of queries where the right page is in the top 3 results. NamedThingBench requires the multi-chunk-dilution family to hit 1.0 — a page with one strong chunk among many weak ones must never be buried.

Range: 0..1, higher is better.

Average rank-1 match score

Key: avg_rank1_score

Plain English: The mean base (pre-boost) retrieval score of the TOP result across recent searches, from gbrain search stats. It is NOT a labeled accuracy number — it is a drift signal: if this trends DOWN over time, retrieval quality is regressing (the early warning that would have caught the duplicate-page incident before a human did).

Range: 0..1. Watch the trend, not the absolute value; pair with the <0.6 / 0.6-0.85 / >=0.85 bucket counts for shape.

Create-safety hint (evidence contract)

Key: create_safety

Plain English: A result's answer to "is this page already in the brain — safe to NOT write a new one?" Derived from the strongest evidence, NOT a raw score: exists (alias_hit / exact_title_match / high_vector_match — do not duplicate), probable (solid keyword match — prefer updating), unknown (weak match — look closer). An agent keys its don't-duplicate decision off this, which is what prevents the incident's duplicate-stub class.

Range: enum: exists | probable | unknown

Set-Similarity / Stability Metrics

Jaccard similarity at k (set Jaccard @k)

Key: jaccard@k

Plain English: How much do two result lists overlap? Compare the top k slugs from the captured baseline against the current run; Jaccard@10 = 1.0 means perfect agreement, 0.0 means zero overlap.

Range: 0..1, higher = more stable. Below 0.5 on a stable corpus means retrieval changed significantly.

Top-1 stability rate

Key: top1_stability

Plain English: Fraction of queries where the #1 result is the same between two runs. The most aggressive stability check — small ranking shifts that don't change the top answer don't hurt it.

Range: 0..1, higher = more stable. Above 0.85 typically means safe-to-merge for retrieval changes.

Statistical-Significance Metrics

p-value (paired bootstrap)

Key: p_value

Plain English: How likely the observed difference between two modes is just noise. Lower = stronger evidence the difference is real. We compute paired bootstrap with 10,000 resamples and Bonferroni correction across the 12 comparisons (3 modes × 4 metrics).

Range: 0..1, lower = stronger signal. Below 0.05 is the common "statistically significant" threshold; below 0.01 is strong evidence.

95% Confidence Interval (CI)

Key: confidence_interval

Plain English: The range we're 95% sure the true value falls inside, given the sample we measured. Narrower CI = more reliable estimate. Computed via bootstrap resampling.

Range: Two-tuple [low, high]. If 0 is inside the CI for a Δ, the difference isn't statistically significant.

Operational / Cost Metrics

Cache hit rate

Key: cache_hit_rate

Plain English: Fraction of searches that reused a recent cached answer instead of running fresh. Higher hit rate = lower latency + lower LLM spend, but stale results may slip through if the threshold is too loose.

Range: 0..1, higher generally better. 0.7-0.9 is the sweet spot for a busy brain; above 0.9 may indicate the similarity threshold is too loose.

Average results returned

Key: avg_results

Plain English: Mean number of search-result rows the engine returned per call. Should be near the active mode's searchLimit unless the brain is small or the budget is dropping results.

Range: 0..searchLimit. Far below searchLimit suggests budget pressure or sparse retrieval.

Average tokens delivered

Key: avg_tokens

Plain English: Estimated tokens (chars / 4) in the chunk text returned per search call. The direct measure of how much context an agent loop is paying for each search.

Range: 0..tokenBudget. Approximates OpenAI tiktoken count for English; off by ~5-10% for Anthropic and worse for non-English.

Cost per query (USD)

Key: cost_per_query_usd

Plain English: Sum of LLM + embedding API charges for one search call. Includes Haiku expansion call (tokenmax mode only) + embedding cost + downstream answer-model cost if measured.

Range: 0..unbounded. Conservative mode is typically <$0.001 per call; tokenmax with answer-gen can exceed $0.01.

p99 latency (ms)

Key: p99_latency_ms

Plain English: 99th percentile wall-clock time per search call. The latency that 1% of users see — long-tail experience, not the average.

Range: 0..unbounded. Warm-cache hits should be <50ms; tokenmax with expansion can exceed 200ms due to the Haiku call.

Result-Sizing Metrics

Autocut signal

Key: autocut.signal

Plain English: Which signal autocut used to size the result set. 'rerank' means it found a real score cliff in the cross-encoder rerank scores and cut there; 'none' means no trustworthy cliff (no reranker, <2 scored results, or the gap was too small) so it returned the full list.

Range: 'rerank' | 'none'. 'none' is not a failure — it means autocut declined to cut because the signal didn't justify it.

Autocut gap ratio

Key: autocut.gap_ratio

Plain English: The size of the largest score drop autocut found, as a fraction of the top result's score. A gap of 0.40 means the score fell by 40% of the top score at the steepest point. Autocut cuts there only when this clears the sensitivity threshold (autocut_jump, default 0.20).

Range: 0..1, higher = a sharper cliff (more confident cut). Below the autocut_jump threshold → no cut.

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Coverage

Every metric printed by any gbrain eval * or gbrain search stats command resolves through getMetricGloss() in src/core/eval/metric-glossary.ts. Adding a new metric to the glossary REQUIRES updating this doc; the CI guard catches drift.