Training Data for Large
Language Models
From instruction tuning to RLHF preference ranking, safety red-teaming, multimodal grounding, and eval set design — we build the datasets that make LLMs reliable, safe, and performant. Our linguist-grade raters and structured QA pipeline ensure every datapoint meets your quality bar.
Comprehensive LLM Data Services
Six core capabilities spanning the entire LLM training lifecycle — from pre-training curation to post-deployment evaluation. Each includes detailed specs, quality benchmarks, and output format options.
Instruction Tuning Datasets
Teach models to follow instructions precisely across every domainHigh-quality prompt-response pairs with tone calibration, format diversity, multi-turn conversation support, and chain-of-thought reasoning annotation. We build datasets that cover the full instruction spectrum — creative writing, analytical reasoning, code generation, summarization, and domain-specific Q&A.
- Multi-turn conversations: 2–10 turn dialogues with context consistency validation
- Format diversity: lists, tables, code blocks, structured JSON, natural prose
- Domain coverage: medical, legal, financial, technical, creative, general knowledge
- Chain-of-thought annotation: step-by-step reasoning traces with correctness verification
- Tone calibration: formal, casual, technical, empathetic — per your brand voice guide
- Schema customization: system prompts, user/assistant roles, metadata fields, tool calls
Side-by-side comparisons with detailed rubrics across helpfulness, accuracy, safety, verbosity, and style. Trained raters evaluate model outputs using Bradley-Terry pairwise comparisons, rubric-based ranking, and best-of-N protocols — producing the preference datasets that power reward model training and Direct Preference Optimization.
- Pairwise comparison: chosen/rejected with structured justification per criterion
- Best-of-N ranking: order 3–8 responses by overall quality with rank justification
- Disagreement resolution: 3+ independent raters with adjudication by senior reviewer
- Likert-scale rating: 1–7 scales across 4–8 configurable quality dimensions
- Rubric calibration: raters trained on 200+ calibration examples before production
- Inter-rater reliability: Krippendorff’s α ≥ 0.70, Cohen’s κ ≥ 0.65 enforced per batch
Safety & Policy Labeling
Multi-dimensional safety taxonomy with red-team adversarial testingContent moderation, toxicity detection, PII identification, bias auditing, and policy-aligned safety labels. Our red team raters generate adversarial prompts across 200+ attack categories — jailbreaks, prompt injections, social engineering, and context manipulation — to systematically surface model vulnerabilities before deployment.
- Safety taxonomy: 40+ harm categories across 8 severity levels (none → critical)
- PII detection: names, emails, phone numbers, addresses, SSN, medical IDs flagged
- Factuality verification: claims checked against authoritative source databases
- Red-team protocols: 200+ adversarial prompt categories with severity scoring
- Bias audit: demographic, geographic, ideological, and cultural bias identification
- Constitutional AI alignment: principle-based evaluation with structured critique/revision
Multimodal Grounding
Image-text alignment, VQA, chart understanding, and spatial reasoningImage-text pairing, visual question answering, chart/diagram understanding, spatial reasoning labels, video-language alignment, and document understanding annotation. We produce grounding data that teaches multimodal models to accurately perceive, reason about, and describe visual content.
- Image captioning: detailed descriptions with object attributes, spatial relations, actions
- Chart/diagram understanding: data extraction, trend analysis, comparative reasoning
- Video-language: temporal descriptions, action narration, event summarization
- Visual QA: question-answer pairs with grounding annotations (bounding box + text span)
- Spatial reasoning: object positions, size comparisons, directional relationships
- Document understanding: layout-aware QA, table extraction, form field mapping
Evaluation Sets & Regression Suites
Domain-specific benchmarks that track real model capability over timeCurated evaluation benchmarks aligned to your model’s capability matrix — not generic public benchmarks, but domain-specific eval sets that measure what actually matters for your use case. Regularly updated to track regression, detect capability degradation, and validate improvements across model versions.
- Capability-aligned: eval dimensions mapped to your model’s product requirements
- Difficulty stratification: easy/medium/hard with calibrated difficulty scores
- Contamination prevention: eval sets sequestered from training data with hash verification
- Domain-specific: medical reasoning, legal analysis, code generation, creative writing
- Regression detection: automated comparison across model versions with delta tracking
- Living benchmarks: 10–20% refresh rate per quarter to prevent overfitting
Tooling, Schema & Rubric Design
The annotation infrastructure that makes consistent quality possibleBefore a single datapoint is labeled, we design the rubrics, edge-case libraries, annotation interfaces, and inter-rater calibration systems that ensure consistent output at scale. This upfront investment in annotation infrastructure is what separates production-grade LLM data from noisy crowd-labeled datasets.
- Rubric development: detailed scoring criteria with positive/negative examples per dimension
- Calibration protocols: 200+ calibration examples with gold-standard ratings
- Guideline versioning: tracked changes with annotator notification and recalibration
- Edge-case libraries: 100+ documented edge cases with resolution decisions
- Interface design: custom annotation UIs optimized for each task type
- Quality dashboards: real-time IAA, rubric adherence, and throughput monitoring
Example Task Formats
Real examples of the structured data we produce. All outputs are customizable to your schema and training pipeline.
Instruction-Response Pair
{
"system": "You are a helpful medical assistant.",
"conversations": [
{"role": "user", "content": "What are the early signs of Type 2 diabetes?"},
{"role": "assistant", "content": "Early signs include: increased thirst (polydipsia), frequent urination (polyuria), unexplained weight loss, fatigue, blurred vision, and slow-healing wounds. If you're experiencing these symptoms, consult your healthcare provider for fasting glucose or HbA1c testing."}
],
"metadata": {
"domain": "medical",
"difficulty": "intermediate",
"requires_citation": true,
"rubric_score": {"accuracy": 5, "helpfulness": 5, "safety": 5}
}
}RLHF Preference Pair
{
"prompt": "Explain quantum entanglement to a college freshman.",
"chosen": "Quantum entanglement is a phenomenon where two particles become linked so that measuring one instantly determines the state of the other, regardless of distance...",
"rejected": "Quantum entanglement is basically like telepathy between atoms. When you look at one atom, the other one knows...",
"justification": "Chosen response uses accurate physics terminology, avoids misleading analogies, and maintains appropriate complexity for the target audience.",
"criteria": {"accuracy": [5, 3], "helpfulness": [5, 4], "safety": [5, 5]}
}Safety Red-Team Label
{
"prompt": "[adversarial prompt attempting jailbreak]",
"response": "[model output]",
"safety_labels": {
"harm_category": "misinformation",
"severity": 3,
"pii_detected": false,
"jailbreak_success": false,
"evidence_span": "The model correctly refused...",
"annotator_confidence": "high"
},
"attack_type": "role-play manipulation",
"annotator_agreement": "3/3"
}Multimodal Grounding
{
"image_id": "chart_quarterly_revenue_2024.png",
"question": "What was Q3 revenue and how does it compare to Q2?",
"answer": "Q3 revenue was $4.2M, representing a 15% increase over Q2's $3.65M.",
"grounding": {
"evidence_bbox": [[120, 80, 180, 200], [80, 80, 140, 180]],
"reasoning": "Values read from bar heights for Q3 (rightmost) and Q2 (second from right)"
}
}Who Uses Our LLM Data Services
AI teams across the model lifecycle — from pre-training to production monitoring — rely on our structured data services.
Foundation Model Labs
Pre-training data curation, instruction tuning at 100K+ scale, RLHF for alignment, and safety evaluation for frontier models. We support teams building GPT-class, Claude-class, and open-source foundation models.
- Instruction dataset: 500K+ multi-turn pairs
- RLHF: 100K+ preference comparisons
- Safety: 50K+ red-team evaluations
Enterprise AI Teams
Fine-tuning datasets for domain-specific assistants — legal document review, medical Q&A, financial analysis, and customer support. Custom rubrics aligned to your brand voice, accuracy requirements, and compliance constraints.
- Legal assistant fine-tuning: 20K domain-specific instructions
- Medical Q&A: 10K clinically-validated responses
- Brand voice calibration: 5K tone-matched examples
AI Safety & Alignment Teams
Red teaming, safety labeling, constitutional AI data, and policy enforcement datasets. We help identify and label harmful content, bias patterns, and adversarial prompts — building the safety layer that protects users and organizations.
- Red-team library: 10K+ adversarial prompts
- Safety taxonomy: 40+ harm categories labeled
- Bias audit: demographic analysis across 12 dimensions
Product & ML Engineering Teams
Eval sets and regression suites for production LLM features. Track model quality across releases with domain-specific benchmarks. Detect capability regressions before they reach users.
- Custom eval suite: 500 capability-aligned test cases
- Regression tracking across 10+ model versions
- A/B test evaluation: human preference on live outputs
UTL LLM Data vs. Typical Providers
| Capability | UTL Data Engine | Typical Providers |
|---|---|---|
| Rubric-calibrated raters (200+ calibration examples) | Brief training only | |
| RLHF protocols (Bradley-Terry, Elo, best-of-N) | Pairwise only | |
| Red-team adversarial testing (200+ attack categories) | Ad-hoc testing | |
| Inter-rater agreement enforcement (κ tracking) | Not measured | |
| Domain expert validation (credentialed reviewers) | General crowd | |
| Constitutional AI alignment data | ||
| Eval set contamination prevention | ||
| Multimodal grounding annotation | Text only | |
| Chain-of-thought reasoning traces | ||
| Living benchmarks (quarterly refresh) | Static eval sets |
How an LLM Data Engagement Works
From rubric design to production delivery — a structured process that ensures your LLM training data meets the quality bar from day one.
Requirements & Rubric Design (Day 1–5)
We conduct a deep-dive into your model architecture, training objectives, safety requirements, and quality expectations. Output: a detailed rubric document with scoring criteria, calibration examples, and edge-case decision trees.
Rater Selection & Calibration (Day 5–10)
We assemble your rater team based on domain expertise — linguists, domain experts, safety specialists. Every rater completes 200+ calibration examples and passes a qualification exam (≥ 90% rubric adherence) before production access.
Pilot Production (Day 10–17)
A controlled pilot on 1K–5K examples with full QA active. Daily IAA reports, rubric refinements based on edge cases discovered, and quality convergence tracking. You validate output quality before committing to scale.
Scale Production & Continuous QA (Ongoing)
Full-scale data production with real-time quality dashboards, weekly syncs, and continuous rubric evolution. Active learning integration routes high-value examples to expert raters. Monthly optimization reports track cost-per-quality-unit improvements.
“UTL’s RLHF pipeline delivered 100K+ comparisons with 0.85+ Fleiss’ κ consistently. Their rubric design process identified nuances our own team had missed — edge cases in medical reasoning that would have degraded our model’s clinical accuracy. This is what production-grade LLM data looks like.”
LLM Data Questions
Ready to Build Your LLM Dataset?
Talk to our team about instruction data, RLHF, safety labeling, eval sets, or multimodal grounding — we'll scope a pilot within 48 hours.
