LLM Benchmarks and Evaluation datasets written by experts
Public benchmarks tell you where a model ranks. They don’t tell you whether it works for your use case. Shaip’s experts write the prompts, reference answers and rubrics that let you measure exactly what matters, release after release.
What is an LLM evaluation benchmark?
An LLM evaluation benchmark is a curated set of prompts, expert-written reference answers and scoring rubrics used to measure how well a model performs on the tasks that matter to your product — release after release. It is also called an evaluation dataset; the golden dataset inside it is the known-good reference answers your outputs are scored against.
Shaip builds custom LLM benchmarks for you: subject-matter experts write and review every item, tag it with the metadata you need to slice results, and run contamination checks — so the benchmark is defensible and the score means something.
What we build
Custom LLM benchmarks and evaluation datasets
Every set is expert-written to your task taxonomy and difficulty distribution — not scraped, not generated and left unchecked.
Golden answer sets
Prompts with expert-written reference answers and acceptable-variation notes.
Used for: automated & human scoring against a known-good answerRubric & scoring guides
Criteria, scales, anchor examples and edge-case rulings.
Used for: consistent human evaluation and LLM-as-a-judge promptsDomain benchmark sets
Hundreds to thousands of prompts spanning the tasks and difficulty levels of one domain.
Used for: release gates, vendor comparisons, regression trackingHeld-out test sets
Prompts kept separate from training data, with contamination checks.
Used for: honest measurement of generalizationAdversarial & edge-case sets
Ambiguous, out-of-scope, policy-boundary and hard-negative prompts.
Used for: robustness and safety measurementRegression suites
Curated items that previously failed, re-run every release.
Used for: catching regressions before users doLLM evaluation metrics
The LLM evaluation metrics we score — and why a human still does
Every benchmark ships with the right metrics attached. Automated proxies from the Shaip Gen AI Platform run at scale; our experts score what automation gets wrong.
Correctness & factuality
Are the claims true and complete?
Why human: domain truth isn't in the reference set; experts catch plausible errors.
Hallucination rate
Share of outputs with unsupported or invented content.
Why human: scorers miss subtle fabrication and over-flag paraphrase.
Relevance & helpfulness
Does the answer serve the user's actual intent?
Why human: intent and helpfulness are judgment calls that vary by domain.
Instruction following
Were constraints, format and scope respected?
Why human: partial compliance and conflicting instructions need adjudication.
Safety & toxicity
Harmful, biased or policy-violating content.
Why human: context-dependent harm (medical, legal) needs expert judgment.
Fluency & language quality
Grammar, naturalness, register, dialect.
Why human: only native speakers judge naturalness and cultural fit.
Preference & win rate
Which of two outputs is better?
Why human: judge bias (position, length, self-preference) needs a human baseline.
Task completion (agents)
Was the goal reached within constraints?
Why human: partial credit and "right answer, wrong method" need review.
Need a different metric?
We design custom metrics and scoring guides for your task.
Why experts write them
Benchmarks written by the experts who know your domain
A benchmark is only as good as its reference answers. Every item is written by one subject-matter expert and reviewed by a second, so the golden answer is defensible and the rubric anticipates the disagreements evaluators will actually have.
Medical professionals
Physicians, nurses and pharmacists write and review the clinical golden sets.
Legal professionals
Lawyers and paralegals author legal reference answers and jurisdiction-aware rubrics.
Financial specialists
Analysts and accountants write numerically precise, compliance-aware benchmark items.
Native speakers
Linguists across 50+ languages write and review in-language golden sets, dialect and all.
Software engineers
Developers write code-correctness reference answers and hard edge-case prompts.
Second-reviewer QA
Every item passes a second expert's review before it enters your evaluation set.
How it works
How an evaluation dataset project runs
A managed, end-to-end build from task taxonomy to a validated, pipeline-ready set.
Define the taxonomy
Agree the task taxonomy and difficulty distribution with your team.
Write & review
Experts draft prompts and reference answers; a second expert reviews every item.
Pilot
Pilot the set on your current model to confirm it separates good outputs from bad.
Deliver with metadata
Domain, task type, difficulty, language and source rationale on every item.
Integrate & re-run
We deliver into your eval harness or CI, with versioning, so the benchmark runs on every release.
What you get
What your evaluation dataset delivers
Training-ready files, not a spreadsheet you have to clean up.
Per-item package
Prompt, reference answer and rubric for every item, in JSONL or CSV.
Sliceable metadata
Slice results by task, domain, difficulty and language.
Scoring guide
A guide your team or an LLM judge can apply consistently.
Pilot results
Evidence of how the set separates strong outputs from weak ones.
Why teams choose Shaip
Data Collection Capabilities
Create, curate, and collect custom-built datasets (text, speech, image, video) from across the globe based on custom guidelines.
Flexible Global Workforce
Leverage 10,000+In-house global workforce and 500K+ Crowd-scale credentialed contributors. Real-time workforce capacity and efficiency.
Quality
Our proprietary platform & skilled workforce use multiple quality control methods to meet or exceed quality standards.
Diverse, Accurate & Fast
Our process streamlines, the collection process through easier task distribution, & data capture directly from the app & web.
Data Security
Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.
End-to-End Annotation
Every collected dataset can be annotated, labeled, transcribed, and validated in the same workflow, delivering model-ready training data.
Security & Compliance
Build your evaluation set
Tell us the tasks that matter and the decision you need to make. We'll scope a custom benchmark, write and review every item, and deliver it pipeline-ready — with a pilot that proves it works.
Contact UsFrequently Asked Questions (FAQ)
1. What is an LLM evaluation dataset or golden dataset?
An LLM evaluation dataset is a curated set of prompts, expert-written reference answers and rubrics used to measure model quality on your tasks. The “golden dataset” is the known-good reference answers your outputs are scored against. Shaip builds both, written and reviewed by subject-matter experts.
2. How large should an evaluation set be?
Typically 300 to 2,000 items per domain. Smaller sets gate releases; larger ones support statistically confident vendor or version comparisons. We scope the size to the decision you need to make.
3. Can you keep the set private?
Yes. Sets are built under NDA and never reused across clients.
4. Do you check for training-data contamination?
Yes. We run overlap checks against the training corpora you share and flag near-duplicates, so a held-out set measures real generalization.
5. Which LLM evaluation metrics do you cover?
Correctness and factuality, hallucination rate, relevance and helpfulness, instruction following, safety and toxicity, fluency, preference and win rate, and task completion for agents — plus custom metrics for your task. Automated proxies run at scale; experts score what automation gets wrong.
6. Which domains and languages do you cover?
20+ domains and 50+ languages, with the same expert-plus-reviewer process in each.
7. Can you start from existing or licensed data?
Yes. Many sets start from Shaip’s off-the-shelf text datasets, then are extended and rubric-tagged by experts.
