Training Data Collection

Comprehensive guide for collecting high-quality training data to improve AI systems

Overview

High-quality training data is essential for developing robust AI systems. This guide covers best practices for collecting, curating, and leveraging human-generated training data.

Key Components

reasoningTraces · ReasoningData

Detailed traces of human expert reasoning processes

Show ReasoningData

stepByStep · string

Explicit documentation of each reasoning step, including:

• Initial problem assessment
• Key considerations and constraints
• Decision points and justifications
• Alternative approaches evaluated

metadata · object

Contextual information about the reasoning process

Show Metadata

domain · string

The knowledge domain (e.g. medical, legal, coding)

expertise · string

Required expertise level and credentials

confidence · number

Expert’s confidence rating (0-1)

codeExamples · CodeData

Programming examples and solutions

Show CodeData

implementation · string

Complete working code solutions

explanation · string

Line-by-line explanations of the implementation

alternatives · string[]

Alternative approaches and tradeoffs

tests · object

Test cases and validation criteria

expertFeedback · FeedbackData

Structured feedback from domain experts

Show FeedbackData

assessment · string

Detailed evaluation of AI system outputs

improvements · string[]

Specific suggestions for enhancement

validation · object

Expert verification of correctness

Collection Methods

Interactive Collection

Gather data through direct expert interaction:

• Real-time problem solving sessions
• Structured interviews and walkthroughs
• Collaborative debugging sessions
• Pair programming exercises

Passive Collection

Automated collection from expert workflows:

• IDE plugins tracking coding patterns
• Browser extensions logging research paths
• Screen recording with audio annotations
• Git commit message analysis

Hybrid Approaches

Combine multiple collection methods:

• Expert review of automated collections
• AI-assisted expert annotations
• Collaborative filtering of examples
• Peer validation workflows

Quality Control

validation · ValidationProcess

Multi-stage validation pipeline

Show ValidationProcess

review · string

Expert peer review of collected data

testing · string

Automated quality checks and tests

scoring · object

Quality metrics and scoring criteria

Integration

api · API

API endpoints for data collection

Show API

submit · endpoint

Submit new training examples

validate · endpoint

Validate collected examples

query · endpoint

Search training dataset

Best Practices

• Document full context for each example
• Capture edge cases and failure modes
• Include negative examples
• Maintain consistent formatting
• Version control all data
• Regular quality audits
• Diverse expert representation

Expert Network

Access to qualified domain experts:

• Software engineers
• ML researchers
• Domain specialists
• Quality assurance
• Technical writers
• Legal experts
• Medical professionals

Security & Privacy

• End-to-end encryption
• Access controls
• Data anonymization
• Audit logging
• Compliance tracking
• Secure storage
• Regular audits

Contact us to learn more about collecting high-quality training data for your AI systems.