1,000 enterprise-grade prompts with substring-checkable answer keys, for benchmarking context governors under the Strategic Alignment Score (SAS) protocol.

File: saseval_v1_prompts.jsonl (1,000 lines, JSON Lines, UTF-8, ~587 KB)
SHA-256: ad7a6b932b8d59dc46483e6e7c53c17253e0b376e9796b4ade5d8ff2cab7460d
Version: v1 · Published by: Cosavu Inc. (ContextAPI Research) · 2026-07-09
Companion paper: SASEval: A Strategic-Alignment-Score Benchmark for Context Infrastructure

What this dataset is (and is not)

SASEval evaluates a context governor — the layer that sits between an application and a downstream LLM and decides which context, memory, and generation policy reach the model under a token/latency budget. This dataset is the test-suite input for that protocol: the prompts a governor must optimize and the gold answers used to grade whether the optimized prompt still yields a correct downstream answer.

✅ It is a stress set of realistic enterprise prompts spanning compression, long-context, multi-doc, conversational, and memory workloads, each with short gold answers.
❌ It is not a model-capability benchmark (like MMLU/HotpotQA). Scores depend on the governor under test and the grading panel you pair with it — not on this data alone.

Composition

Exactly 100 prompts per category, 10 categories (uniform, N = 1,000):

Category	N	Character
`simple_factual`	100	Short enterprise/technical Q&A, low compressibility
`bloated_factual`	100	A simple question wrapped in corporate-email filler/politeness
`reasoning`	100	Multi-step quantitative business problems (SLA math, unit economics, capacity)
`bloated_reasoning`	100	Same, wrapped in verbose filler
`technical_code`	100	Code review, debugging, API/config; short-token answers
`long_document_qa`	100	250–450-word enterprise doc (policy/postmortem/SOW/RFC/SLA) + question
`multi_document_qa`	100	4–6 labeled snippets with distractors + question
`conversational`	100	2–4 turn support dialog ending on an implicit-context question
`memory_recall`	100	Stored profile/account/config facts + retrieval question
`memory_update`	100	Stale fact → correction → question requiring the latest value

Enterprise domains sampled include cloud/DevOps, fintech & payments, HIPAA SaaS, logistics, IAM/security, data platforms, e-commerce, telecom, manufacturing ERP, insurance, CRM, and legal.

Schema (per JSON line)

Field	Type	Description
`id`	string	Stable id, e.g. `sas1k-0421`
`category`	string	One of the 10 categories above
`prompt`	string	The user prompt fed to the governor (may embed a document/dialog/memory block)
`golds`	string[]	Acceptable short answers; grading is case-insensitive substring match against any entry
`target_action`	float[5]	Category-conditioned SAS solvency target `[c, T, p, d, L/2048]`
`T`	float	Canonical grader temperature for the category
`top_p`	float	Canonical nucleus-sampling top-p
`mode`	string	`DEEP` (reasoning) or `STRICT` (direct)
`max_new`	int	Canonical max answer tokens for the category
`durable`	string[]	(memory categories) facts a correct governor must retain
`stale`	string[]	(memory categories) outdated facts a correct governor should drop
`_gold_repaired`	bool	(present when true) numeric key was replaced by the verification pass
`_needs_review`	bool	(present when true) item flagged as likely ill-posed — filter before use

Category target actions follow Appendix B of the SASEval paper, e.g.:

simple_factual      c=0.10 T=0.30 p=0.90 d=0 L=128
reasoning           c=0.20 T=0.20 p=0.90 d=1 L=768
long_document_qa    c=0.45 T=0.20 p=0.90 d=1 L=512
multi_document_qa   c=0.55 T=0.20 p=0.90 d=1 L=512

Example records

{"id":"sas1k-0881","category":"memory_update",
 "prompt":"The security policy previously required Kubernetes clusters to use version 1.22. A recent update mandates a minimum version of 1.25. What is the current minimum supported Kubernetes version for new clusters?",
 "golds":["1.25","kubernetes 1.25","version 1.25"],
 "target_action":[0.45,0.2,0.9,1,0.25],"T":0.2,"top_p":0.9,"mode":"DEEP","max_new":512,
 "durable":["1.25"],"stale":["1.22"]}

How it was built

Authoring (batch): qwen/qwen3-32b generated the prompts and gold answers via the Groq Batch API (253 requests), category-by-category with enterprise domain rotation and a strict "short canonical golds" instruction.
Numeric verification (batch): every reasoning / bloated_reasoning item was independently re-solved by llama-3.3-70b-versatile at T=0. Where the independent solve disagreed with the authored key, the key was replaced — 71 of 200 numeric golds (35.5%) were repaired, confirming LLM self-authored arithmetic keys are unreliable without a check.
Assembly: de-duplicated by prompt, trimmed to 100/category, target actions attached deterministically, _needs_review flags added heuristically for ill-posed rate/percentage items.

Reproduction scripts: saseval_gen_1k.py (authoring) and saseval_repair.py (verification/top-up), in the repository root.

Intended use

Primary: run the SASEval protocol — send each prompt to a context governor, forward the optimized prompt to a frozen 3-model LLM panel, grade panel answers against golds by substring match, and compute per-prompt SAS = 700·Q + 200·C·ρ + 100·s.
Also useful for: prompt-compression evaluation, long-context/multi-doc retention tests, and memory-freshness diagnostics (durable/stale).

Reference pilot result (context)

An N=20 stratified pilot on the live api.cosavu.com STAN governor (panel: Llama-3.3-70B, Qwen3-32B, GPT-OSS-120B via Groq) produced the ordering stan-1.5-mini-thinking (799.6) ≻ predictive (780.3) ≻ Identity (763.7) ≻ instant (626.0) ≻ random-drop (561.9), stable across all weight and panel leave-one-out perturbations. The full N=1,000 run is pending.

Limitations & known issues

LLM-authored keys. Keys are model-generated. The reasoning/bloated_reasoning slice is the least reliable; despite the verification pass, treat it as the highest-risk category and prefer a human/deterministic recheck for production leaderboards.
_needs_review (14 items). Rate/percentage questions where the verifier returned a large integer — likely ill-posed. Filter these out for strict use.
Substring grading is lenient. Some long_document_qa / multi_document_qa items list several distinct facts in golds rather than variants of one answer, so a mention of any counts as correct. ~41 gold strings exceed 4 words.
Memory annotations partial. 176/200 memory items carry both durable and stale; the rest carry durable only.
English only; synthetic. Enterprise scenarios are realistic but fictional; no PII, no real customer data.

Grading normalization (reference)

import re
def norm(t): return re.sub(r"[^a-z0-9 ]", " ", t.lower())
def graded(answer, golds): 
    n = norm(answer)
    return int(any(norm(g) in n for g in golds))   # 1 if any gold is a substring

Licensing

Released by Cosavu Inc. for benchmark evaluation. Confirm the intended distribution license before public release (the frontmatter marks it other/cosavu-eval as a placeholder).

Citation

@techreport{saseval2026,
  title  = {SASEval: A Strategic-Alignment-Score Benchmark for Context Infrastructure},
  author = {Abimalla, Thishyaketh and Teja, Arun and Komal, Satya},
  institution = {Cosavu Inc., ContextAPI Research Division},
  year   = {2026}
}

Changelog

v1 (2026-07-09): initial 1,000-prompt release; 71 numeric keys repaired; 14 items flagged _needs_review. Dataset SHA-256 ad7a6b93…b99bbd3.

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