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Section 9

Engineering

A benchmark is only as trustworthy as the system underneath it. This is that system: what was chosen, what it was measured at, and what each choice cost.

Every figure here is either read from a committed file or recorded in the build log. Nothing is projected, rounded up, or copied from a vendor benchmark. Where a number has not been measured yet the entry says so instead of estimating one.

Everything runs on one machine: NVIDIA RTX 5060 Laptop, 8 GB VRAM. The constraint is the point. Every decision below — fp16, batch size, model choice, co-residency — was forced by an 8 GB budget rather than chosen on a machine where it did not matter.

9.1 Decisions, and what they cost

Each entry names the alternative it was chosen over. A decision recorded without its alternative is a preference; recorded with one, it is an argument that can be checked.

Vector index

HNSW at m=32, ef_construction=200, ef_search=200

measured

Instead of
pgvector defaults (m=16, ef_construction=64, ef_search=40)
Evidence
Measured on a tuning sweep: defaults reached 0.836 overall recall, the tuned parameters reached 0.998.
Cost
Index build cost roughly triples. Paid once per ingest, never on the query path.
Why
A vector index shipped on defaults is an untested assumption. The sweep turned it into a number with a price attached.

Fusion

Reciprocal rank fusion over ranks, k=60

argued, not measured

Instead of
Normalising and summing BM25 and cosine scores
Evidence
BM25 scores and cosine similarities are not on comparable scales; any normalisation encodes an assumption that is wrong differently for every query.
Cost
Discards score magnitude, keeping only order.
Why
Fusing by position needs no assumption about either scale. It is the cheaper claim to defend.

Generation

5 passages of 700 characters, max 2,048 output tokens, temperature 0

measured

Instead of
Ten passages of 1,200 characters
Evidence
A 3B model given ten 1,200-character passages produced no claims at all. Fewer, shorter passages left it enough attention to copy a quote accurately.
Cost
Less context per answer, so recall of the retriever matters more.
Why
Verbatim quoting is what the citation check verifies. A context budget that defeats quoting defeats the verification.

Generation

Schema-constrained decoding via XGrammar under vLLM

argued, not measured

Instead of
Asking for JSON in the prompt and parsing what comes back
Evidence
The answer schema becomes a decoding constraint rather than a request, so citations are parseable by construction.
Cost
Ties generation to a serving stack that supports grammars.
Why
A citation format that depends on the model's goodwill is not a format.

Embedding

fp16, batch 64, on bge-small-en-v1.5 (384-dimension)

measured

Instead of
fp32, or a larger embedding model
Evidence
51,310 chunks embedded in 1.5 minutes at 558 chunks/s, producing 79 MB of vectors. 6.87 GB of the 8 GB budget remained free with the embedder loaded.
Cost
A smaller embedding model than the leaderboards favour.
Why
Headroom was the requirement: the embedder and the generator have to be co-resident on one 8 GB card.

Access control

PostgreSQL row-level security inside the retrieval query, under a NOSUPERUSER NOBYPASSRLS role

measured

Instead of
Filtering results in application code after retrieval
Evidence
The application connection asserts rolsuper=false and rolbypassrls=false before it is handed to a caller. A superuser is exempt from RLS silently — no error, no warning.
Cost
Post-filtering an approximate index costs recall, which is measured and reported separately rather than folded into headline numbers.
Why
Access control enforced in application code is a control that a bug can skip. Enforced in the query, it is a control the database applies.

Access control

One partition and one index per tenant

argued, not measured

Instead of
A single index across all tenants on the parent table
Evidence
A partitioned index on the parent is built across every tenant, which loses the property that makes partitioning useful.
Cost
More indexes to build and maintain.
Why
A tenant's data being in its own partition is what makes isolation structural rather than a WHERE clause.

9.2 How the numbers are kept honest

The measurement apparatus is the part of this project that took the longest and is the easiest to skip. Every practice below exists because skipping it produces a number that looks fine and is not.

One factor per variant
Each published run differs from the baseline by exactly one configuration key, so any difference is attributable to that key. The comparison view flags it when more than one differs.
Bootstrapped 95% confidence intervals
1,000 resamples of the query set on every quality metric. A delta whose intervals overlap is reported as not separated rather than given a number that looks decisive.
The judge is measured, not trusted
Relevance grades are drafted by a pinned model and a stratified sample is re-graded by hand. Of 32 judgments checked so far the human and the model agreed on 4. No retrieval score is published until that is understood.
Failure modes are published, not hidden
Nine documented failure modes, each marked observed or prevented-by-design, each naming the metrics it corrupts and how it was detected.
Every figure carries its provenance
Script, regenerate command, commit and corpus id sit beside each figure, so a number can be traced to the code that produced it.
Absent numbers stay absent
No placeholder figures and no illustrative values anywhere. A run that has not happened renders as an empty state naming the phase that delivers it.

9.3 Guardrails

A retrieved passage is untrusted input. It reaches a prompt, and anything in it that reads as an instruction can be followed. These are the boundaries that assumption produces.

Instruction stripping at ingestion
Six narrow pattern classes — override, role reassignment, chat-role markers, tag injection, exfiltration, answer override. Removals are counted and marked in the chunk, never silent, and the patterns are deliberately narrow so ordinary contract and standards prose survives.
Retrieved text cannot forge prompt structure
Each passage is fenced and the fence literal is stripped from the body first, so a document cannot impersonate the header of a passage that was never retrieved.
Citations must resolve to passages actually shown
A cited chunk id is checked against the set the model was given. A citation to anything else fails rather than being displayed.
Quotes are checked character by character
Each quoted span is matched against the source text, with the longest verbatim run recorded so an altered quote is distinguishable from an invented one.
Abstention is a measured outcome
The system declines when the evidence does not support an answer, and how often it declines correctly is one of the reported metric families rather than a footnote.

9.4 The stack, pinned

Versions are pinned because a benchmark whose dependencies float is not reproducible. A model that silently updates changes every number measured against it.

Layer and the pinned choice for each
LayerPinned to
EmbeddingBAAI/bge-small-en-v1.5, 384-dim, fp16
RerankingBAAI/bge-reranker-base cross-encoder
Vector indexPostgreSQL 16 with pgvector 0.8.6, HNSW
Lexical indexPostgres GIN over tsvector, BM25-style scoring
ServingvLLM 0.27.1 with XGrammar constrained decoding
Access controlRow-level security, non-superuser role, per-tenant partitions
Golden setGemini, pinned to dated snapshots, human-verified sample
SiteNext.js static export, no runtime, no backend

ABS Abstention

Abstention (correct)

What it measures

Proportion of genuinely unanswerable queries the system correctly declines.

Formula

Abst = |{ q in U : system declined }| / |U|
U
the unanswerable subset of the query set
declined
the abstention gate fired rather than an answer being produced

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

abstention_correct()

Arrives in Phase 4.

What can go wrong

Related — Abstention family

faithfulness · recall_at_10

GRD Grounding

Answer correctness

What it measures

Agreement with the reference answer, judged against a published rubric.

Formula

correctness = judge(answer, reference) in {0, 1}
reference
the golden set answer, model-drafted and partially human-verified

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

answer_correctness()

Arrives in Phase 4.

What can go wrong

Related — Grounding family

answer_relevance · faithfulness

GRD Grounding

Answer relevance

What it measures

Whether the answer addresses the question that was actually asked.

Formula

relevance = judge(answer, question) in {0, 1}
judge
LLM against a published rubric; the model reference is pinned per run

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

answer_relevance()

Arrives in Phase 4.

What can go wrong

Related — Grounding family

answer_correctness

GRD Grounding

Citation accuracy

What it measures

Proportion of citations that resolve to a chunk actually supporting the cited claim.

Formula

A = |{ citations resolving to a supporting chunk }| / |citations|
resolve
the cited chunk id exists in the index for this run
supporting
string containment first; LLM judge only where containment fails

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

citation_accuracy()

Arrives in Phase 2.

What can go wrong

Related — Grounding family

citation_coverage · faithfulness

GRD Grounding

Citation coverage

What it measures

Proportion of factual claims that carry a citation at all.

Formula

C = |{ claims with >= 1 citation }| / |factual claims|
factual claim
an assertion that could be checked against a source

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

citation_coverage()

Arrives in Phase 2.

What can go wrong

Related — Grounding family

citation_accuracy · faithfulness

RET Retrieval

Context precision

What it measures

Proportion of retrieved chunks that are actually relevant.

Formula

P(q) = |{ c in top_k(q) : grade(c) >= 2 }| / k
k
retrieval depth, recorded per run in the config

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

context_precision()

Arrives in Phase 4.

What can go wrong

Related — Retrieval family

recall_at_10 · faithfulness

CST Cost

Cost per query

What it measures

Token counts multiplied by the rates in the run config. Local models cost zero, stated openly.

Formula

cost = (in_tokens * rate_in + out_tokens * rate_out) / 1e6
rate
from configs/pricing.yaml, recorded per run
local models
0.00 by definition; hardware cost is not amortised into this number

US dollars per query. Zero for fully local runs, which is stated rather than hidden.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

Reported as a point estimate. This is a measured quantity of the run rather than a sample statistic over queries.

How this project computes it

cost_per_query()

Arrives in Phase 4.

What can go wrong

Related — Cost family

latency_p95_s

GRD Grounding

Faithfulness

What it measures

Proportion of generated claims entailed by the retrieved chunks.

Formula

F(a) = |{ claims in a entailed by context }| / |claims in a|
claims
atomic assertions extracted from the answer
entailed
judged by an LLM against a published rubric, not string match

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

faithfulness()

Arrives in Phase 4.

What can go wrong

Related — Grounding family

citation_accuracy · citation_coverage · answer_correctness

PRF Performance

p95 latency

What it measures

95th percentile end-to-end time, retrieval through verification.

Formula

p95 = quantile(latencies, 0.95) over 3 repeats per query
end-to-end
retrieval, rerank, generation, citation resolution, verification
repeats
three per query; the distribution is over all of them

Wall clock seconds. Lower is better. Machine-dependent, so it is only comparable within a run set.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

Reported as a point estimate. This is a measured quantity of the run rather than a sample statistic over queries.

How this project computes it

latency_percentiles()

Arrives in Phase 4.

What can go wrong

Related — Performance family

cost_per_query_usd

RET Retrieval

MRR@10

What it measures

Mean reciprocal rank of the first relevant chunk. Rewards getting one right answer high.

Formula

MRR@k = (1/|Q|) * sum_q 1 / rank_first_relevant(q)
rank_first_relevant(q)
1-indexed rank of the first chunk graded >= 2, or 0 contribution if none

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

mrr_at_k()

Arrives in Phase 4.

What can go wrong

Related — Retrieval family

recall_at_10 · ndcg_at_10

RET Retrieval

NDCG@10

What it measures

Discounted cumulative gain over graded relevance, normalised against the ideal ranking.

Formula

DCG@k = sum_i (2^grade(c_i) - 1) / log2(i + 1)
NDCG@k = DCG@k / IDCG@k
c_i
the chunk at rank i
IDCG@k
DCG of the best possible ordering
grade
0-3, so a 3 counts 7x a 1

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

ndcg_at_k()

Arrives in Phase 4.

What can go wrong

Related — Retrieval family

recall_at_10 · mrr_at_10

RET Retrieval

Recall@10

What it measures

Proportion of queries where at least one chunk graded 2 or higher appears in the top 10.

Formula

R@k = |{ q in Q : max grade(c) >= 2 for c in top_k(q) }| / |Q|
grade(c)
graded relevance 0-3 from the golden set
top_k(q)
the k chunks the retriever ranked highest for q
Q
the answerable query set

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

recall_at_k()

Arrives in Phase 4.

What can go wrong

Related — Retrieval family

recall_at_5 · ndcg_at_10 · mrr_at_10 · context_precision

RET Retrieval

Recall@5

What it measures

The same measure at rank 5. Harder, and more sensitive to reranking.

Formula

R@5 = |{ q in Q : max grade(c) >= 2 for c in top_5(q) }| / |Q|
top_5(q)
the five chunks ranked highest for q

Mathematical range. No typical value is stated: it would be a number with no run behind it.

Worked example

From the golden set, cross-document, query cross-document-000 — 8 human-verified judgments.

What was the total operating income for the North America segment in fiscal 2023, and which XBRL financial taxonomy members are associated with cash flow hedging using foreign exchange contracts during the same fiscal period?

  • grade 3edgar-0000829224-000082922424000057::fixed-512::00015
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00108
  • grade 3edgar-0000320187-000032018725000047::fixed-512::00183
  • grade 3edgar-0000320187-000032018724000044::fixed-512::00185
  • grade 3edgar-0000796343-000079634324000006::fixed-512::00150
  • grade 3edgar-0000796343-000079634325000004::fixed-512::00152

Confidence interval

95% interval by bootstrap resampling over 1,000 resamples of the query set. A point estimate over a few hundred queries without an interval is the first thing a reviewer attacks.

How this project computes it

recall_at_k()

Arrives in Phase 4.

What can go wrong

Related — Retrieval family

recall_at_10 · ndcg_at_10