Retrieval Recall@k

status="ready" — 2,000 support questions, 512-token chunks, three retrievers swept at k = 1, 3, 5, 10, 20, 50, 100. Hybrid holds the highest recall at all seven measured k. Click a k chip, or Tab to the group and use the arrow keys: the rule, both panels' dots, every row and the sentence under them all move to that cut.

RAG retrieval sweep · support KB v7

2,000 queries · k 1–100 · shipped k = 10 · 512-token chunks

shipped k = 10 · 5,120 tokens per query

at k = 10 · 5,120 tokens of retrieved context per query

BM25 · lexical
recall 0.574precision 0.107MRR not reported1.1 of 10 chunks relevant
57% inside k = 10 · 24% only by k = 100 · 19% still missing at k = 100
Dense · e5-large-v3
recall 0.729precision 0.137MRR 0.6761.4 of 10 chunks relevant
73% inside k = 10 · 18% only by k = 100 · 9% still missing at k = 100
Hybrid · RRF + rerankleads recall here
recall 0.796precision 0.150MRR 0.7431.5 of 10 chunks relevant
80% inside k = 10 · 15% only by k = 100 · 5% still missing at k = 100

At k = 10, Hybrid · RRF + rerank finds 0.796 of the relevant chunks, and precision 0.150 puts 1.5 relevant chunks among the 10 chunks retrieved. Widening to k = 20 buys +0.058 recall for 5,120 more tokens per query. Its recall is within 5% of its 0.951 ceiling from k = 50 on.

recall@k can only rise with k and precision@k almost always falls, so the pair is a budget question, not a quality one. MRR@k counts a query as zero when its first relevant chunk falls past k, which is why it climbs with k and then flattens.

Retrieval sweep: recall@k above precision@k, k from 1 to 100 on a log axis, 3 retrievers. At k = 10, Hybrid · RRF + rerank reaches recall 0.796 and precision 0.150. Hybrid · RRF + rerank holds the highest recall at 7 of 7 measured k. Exact values follow in the data table. Each retriever lists up to 12 cuts, evenly spaced by rank.
Retriever	k	recall@k	precision@k	MRR@k
BM25 · lexical — 7 cuts, recall ceiling 0.809 at k = 100	1	0.215	0.374	not reported
BM25 · lexical	3	0.420	0.259	not reported
BM25 · lexical	5	0.491	0.184	not reported
BM25 · lexical	10	0.574	0.107	not reported
BM25 · lexical	20	0.648	0.061	not reported
BM25 · lexical	50	0.742	0.028	not reported
BM25 · lexical	100	0.809	0.015	not reported
Dense · e5-large-v3 — 7 cuts, recall ceiling 0.909 at k = 100	1	0.344	0.582	0.582
Dense · e5-large-v3	3	0.582	0.360	0.658
Dense · e5-large-v3	5	0.655	0.246	0.669
Dense · e5-large-v3	10	0.729	0.137	0.676
Dense · e5-large-v3	20	0.795	0.075	0.679
Dense · e5-large-v3	50	0.863	0.032	0.680
Dense · e5-large-v3	100	0.909	0.017	0.681
Hybrid · RRF + rerank — 7 cuts, recall ceiling 0.951 at k = 100	1	0.399	0.660	0.660
Hybrid · RRF + rerank	3	0.669	0.415	0.729
Hybrid · RRF + rerank	5	0.731	0.276	0.738
Hybrid · RRF + rerank	10	0.796	0.150	0.743
Hybrid · RRF + rerank	20	0.854	0.081	0.746
Hybrid · RRF + rerank	50	0.914	0.035	0.747
Hybrid · RRF + rerank	100	0.951	0.018	0.747

unvalidated feed — the lexical baseline was only logged from k = 5, a sparse model was queued and returned nothing, three cuts arrived unplottable (k = 0, a recall that is not a number, a second row for a k already taken), and the shipped operatingK of 12 was never measured. Each of those is named under the chart rather than smoothed away.

Retrieval sweep · unvalidated eval feed

2,000 queries · k 1–100 · shipped k = 10 · 512-token chunks

shipped k = 10 · 5,120 tokens per query

at k = 10 · 5,120 tokens of retrieved context per query

BM25 · lexical
recall 0.574precision 0.107MRR not reported1.1 of 10 chunks relevant
57% inside k = 10 · 24% only by k = 100 · 19% still missing at k = 100
Dense · e5-large-v3
recall 0.729precision 0.137MRR 0.6761.4 of 10 chunks relevant
73% inside k = 10 · 18% only by k = 100 · 9% still missing at k = 100
Hybrid · RRF + rerankleads recall here
recall 0.796precision 0.150MRR 0.7431.5 of 10 chunks relevant
80% inside k = 10 · 15% only by k = 100 · 5% still missing at k = 100
SPLADE v3 · sparse
no k measured — the run returned nothing

At k = 10, Hybrid · RRF + rerank finds 0.796 of the relevant chunks, and precision 0.150 puts 1.5 relevant chunks among the 10 chunks retrieved. Widening to k = 20 buys +0.058 recall for 5,120 more tokens per query. Its recall is within 5% of its 0.951 ceiling from k = 50 on.

recall@k can only rise with k and precision@k almost always falls, so the pair is a budget question, not a quality one. MRR@k counts a query as zero when its first relevant chunk falls past k, which is why it climbs with k and then flattens.

The payload ships operatingK 12, which this sweep never measured; the nearest measured cut, k = 10, is marked instead.

3 cuts were dropped: a k below 1, a rate outside 0–1, a value that is not a finite number, or a repeat of a k already taken.

Retrieval sweep: recall@k above precision@k, k from 1 to 100 on a log axis, 3 retrievers. At k = 10, Hybrid · RRF + rerank reaches recall 0.796 and precision 0.150. Hybrid · RRF + rerank holds the highest recall at 7 of 7 measured k. SPLADE v3 · sparse arrived with no k at all. 3 cuts could not be placed. Exact values follow in the data table. Each retriever lists up to 12 cuts, evenly spaced by rank.
Retriever	k	recall@k	precision@k	MRR@k
BM25 · lexical — 5 cuts, recall ceiling 0.809 at k = 100	5	0.491	0.184	not reported
BM25 · lexical	10	0.574	0.107	not reported
BM25 · lexical	20	0.648	0.061	not reported
BM25 · lexical	50	0.742	0.028	not reported
BM25 · lexical	100	0.809	0.015	not reported
Dense · e5-large-v3 — 7 cuts, recall ceiling 0.909 at k = 100	1	0.344	0.582	0.582
Dense · e5-large-v3	3	0.582	0.360	0.658
Dense · e5-large-v3	5	0.655	0.246	0.669
Dense · e5-large-v3	10	0.729	0.137	0.676
Dense · e5-large-v3	20	0.795	0.075	0.679
Dense · e5-large-v3	50	0.863	0.032	0.680
Dense · e5-large-v3	100	0.909	0.017	0.681
Hybrid · RRF + rerank — 7 cuts, recall ceiling 0.951 at k = 100	1	0.399	0.660	0.660
Hybrid · RRF + rerank	3	0.669	0.415	0.729
Hybrid · RRF + rerank	5	0.731	0.276	0.738
Hybrid · RRF + rerank	10	0.796	0.150	0.743
Hybrid · RRF + rerank	20	0.854	0.081	0.746
Hybrid · RRF + rerank	50	0.914	0.035	0.747
Hybrid · RRF + rerank	100	0.951	0.018	0.747
SPLADE v3 · sparse	no k recorded

status="loading" — the skeleton keeps both panels, the rising recall silhouette over the decaying precision one, and a row of k chips, so nothing shifts when the sweep lands

RAG retrieval sweep · support KB v7

recall@k and precision@k against the cut of the ranked list

status="empty" — no k measured yet; the copy says what this card needs (rates at a ladder of cuts) rather than reading as a failed fetch

RAG retrieval sweep · support KB v7

recall@k and precision@k against the cut of the ranked list

No k measured yet

This card sweeps k — how many documents the retriever hands the generator — so it needs recall@k and precision@k at a ladder of cuts, per retriever. A ranker has no score threshold to move, so a single (recall, precision) pair is not a point on this chart.

status="error" — click Try again

RAG retrieval sweep · support KB v7

recall@k and precision@k against the cut of the ranked list

Couldn't load this sweep

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