Chain-of-Thought Datasets for Reasoning AI
Expert-written reasoning chains across math, logic, and domain-specific problem types. Built for fine-tuning LLMs to produce correct, step-by-step reasoning in production AI systems.
20+
Years in AI training data
1,000+
Language locales
1M+
Hours of video annotated
ISO
27001 certified
The Challenge
Beyond Public Reasoning Benchmarks
GSM8K and MATH benchmarks drove progress in LLM mathematical reasoning. Fine-tuning reasoning AI for domain-specific problem types requires expert-written step-by-step solutions your benchmarks do not cover.
Medical diagnosis reasoning, legal case analysis, and financial modelling follow domain-specific reasoning patterns. LLMs fine-tuned on math word problems produce poor reasoning chains when applied to clinical decision support or regulatory analysis.
Off-the-shelf chain-of-thought datasets suffer from domain narrowness (mathematics and commonsense only) and reasoning quality variance from automated or crowd-sourced solution generation.
LXT builds custom chain-of-thought datasets with expert-written reasoning chains for your domain's problem types, drawing on a vetted supplier network with real chain-of-thought reasoning data. We deliver verified step-by-step solutions that teach your LLM the correct reasoning pattern for your specific deployment context.
Why Teams Upgrade
Limitations of Public Chain-of-Thought AI Datasets
Standard benchmarks serve research well. Production deployments need more.
| Dataset | Primary Limitation | Impact |
|---|---|---|
| GSM8K | Elementary math word problems only; single-domain; automated solution generation for many examples; limited to arithmetic reasoning | Math-only |
| MATH (Hendrycks) | Competition mathematics; very high difficulty ceiling; limited to STEM reasoning patterns with no domain generalization | Competition-level |
| ARC Challenge | Science multiple-choice only; no explicit reasoning chain annotation; short answer format limits reasoning depth | No chains |
| StrategyQA | Wikipedia-grounded commonsense only; yes/no answers; limited reasoning chain depth and domain applicability | Commonsense-only |
| CommonsenseQA | Multiple-choice commonsense only; no step-by-step solutions; limited to everyday knowledge reasoning patterns | No steps |
Not sure which specs you need?
Our data specialists help you scope the right dataset for your model architecture.
Configurable Specifications
Specs Built Around Your Model
Public datasets come fixed. Yours is configured for your architecture, environment, and use case.
Problem Types
Reasoning Coverage
- Domain Problems: Domain-specific reasoning tasks matched to your LLM's target application
- Difficulty Tiers: Easy, medium, and hard problem distributions per reasoning category
- Multi-Step Depth: 2-step to 10-step reasoning chains with intermediate conclusion verification
Reasoning Quality
Expert Standards
- Expert Writers: Domain specialists write and verify step-by-step solutions
- Step Verification: Each reasoning step verified for logical validity and factual accuracy
- Error Analysis: Common reasoning errors annotated as negative examples where applicable
Format Options
Chain Configuration
- Scratchpad: Natural language reasoning steps before final answer
- Structured: Numbered steps with premise, inference, and conclusion labels
- Code-Based: Python or pseudocode reasoning chains for quantitative tasks
Need a custom configuration?
We've built datasets across dozens of domains and use cases. Let's scope yours.
Capturing Complexity
Edge Cases in Reasoning Datasets
High-accuracy models handle rare attributes that public datasets miss.
Multi-Step Dependency Chains
Later reasoning steps depend on correct earlier steps. Each intermediate conclusion is verified independently before the chain is accepted, preventing error propagation in training data.
Ambiguous Problem Statements
Real-world problems are often underspecified. Annotation guidelines define how to handle ambiguous problems, including how to request clarification or state reasonable assumptions explicitly.
Domain-Specific Notation and Conventions
Legal citation reasoning, medical differential diagnosis, and financial ratio analysis use domain-specific conventions. Expert annotators familiar with domain conventions produce correct reasoning chains.
Counterfactual and Hypothetical Reasoning
Problems asking what would happen under different conditions require careful annotation of hypothetical reasoning steps distinct from factual assertions.
Ground Truth Quality
Human-in-the-Loop Annotation
Precise annotation bridges raw data and learnable signal. Expert annotators deliver precision automated tools can't match.
Expert Reasoning Chain Writing
Domain specialists write complete step-by-step solutions. Each chain independently verified for logical validity and factual accuracy before inclusion.
Step-Level Verification
Each individual reasoning step verified against domain knowledge. Incorrect intermediate steps that lead to correct answers are flagged and corrected.
Error Example Annotation
Annotated incorrect reasoning chains with labeled error types (calculation error, false premise, invalid inference) for training models to detect and avoid common errors.
Industry Applications
Chain-of-Thought Datasets for Your Domain
Custom taxonomies and collection protocols for specific deployment contexts.
Medical Diagnosis
Differential reasoning, clinical decision support
Legal Reasoning
Case analysis, statute application, contract interpretation
Financial Analysis
Investment thesis, risk assessment, regulatory reasoning
Code Reasoning
Algorithmic problem solving, debugging chains
Scientific AI
Hypothesis generation, experimental reasoning
Data Analysis
Statistical reasoning, interpretation chains
EdTech
Step-by-step tutoring, worked examples
General LLM Reasoning
Broad reasoning improvement for base model fine-tuning
Compliance & Ethics
Secure and Ethical Data Collection
Data collection involving people and sensitive content requires robust security, compliance, and ethical protocols at every stage.
Global Demographic Reach
Collection across 1,000+ locales and diverse demographics to prevent algorithmic bias in your deployed models.
ISO 27001 Certified
Sensitive projects processed in certified secure facilities meeting the highest information security standards.
GDPR & Privacy Compliance
All collection and annotation protocols vetted for consent and privacy. Legally robust for global deployment.
Frequently Asked Questions
Chain-of-Thought Dataset FAQs
How do you verify the correctness of reasoning chains?+
Each chain is reviewed by a second domain expert who checks every step independently. For quantitative problems, we use automated verification of final answers as a secondary check.
Can you produce reasoning chains in a specific format?+
Yes. We support natural language scratchpad format, numbered step format, XML/JSON structured reasoning, and code-based reasoning formats depending on your fine-tuning architecture.
What domains do your reasoning annotators cover?+
We have domain specialists for mathematics, medicine, law, finance, and STEM fields. For specialized sub-domains, we recruit and calibrate subject matter experts for your project.
How many examples do I need for reasoning improvement?+
For domain-specific reasoning fine-tuning, 500-5,000 high-quality chain-of-thought examples typically produce meaningful improvement. We advise on volume and format based on your base model and task.
Can you include negative examples with incorrect reasoning?+
Yes. We annotate incorrect reasoning chains with labeled error types for training models to detect and avoid common reasoning errors. Negative example sets are available as add-ons.
What does a custom chain-of-thought dataset cost?+
Projects range from $25K for focused domain datasets (500-2,000 problems with solutions) to $150K+ for large-scale multi-domain reasoning datasets.
Can you build evaluation benchmarks as well as training sets?+
Yes. We build separate held-out evaluation sets following the same quality standards, with difficulty-stratified problems and expert-verified gold answers for rigorous model evaluation.
Get Started
Scope Your Custom Chain-of-Thought Dataset
Share your reasoning domain, problem types, and target difficulty. A reasoning AI data specialist will provide a detailed proposal within 48 hours.
Please provide us with the details of your inquiry and one of our team members will be in touch.
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