
Kili Technology
The data-centric AI platform for high-quality training data and model evaluation.

Scriptable machine teaching and active learning for production-grade AI training data.

AI Data Prodigy, developed by the architects behind spaCy (Explosion), represents the gold standard in scriptable machine teaching for 2026. Unlike cloud-based black-box solutions, Prodigy is a developer-first tool that runs entirely on-premise or in private clouds, ensuring maximum data security and privacy. Its core architecture leverages active learning, where the model only asks for human intervention on the most uncertain data points, drastically reducing annotation time by up to 10x. By 2026, the platform has evolved to include native 'LLM-in-the-loop' workflows, allowing users to verify and refine model outputs rather than labeling from scratch. This makes it a critical component in the RLHF (Reinforcement Learning from Human Feedback) pipeline for enterprises building proprietary vertical LLMs. Its extensible Python API allows data engineers to write custom annotation 'recipes,' integrating seamlessly into CI/CD pipelines for continuous model improvement. The tool's focus on small, high-quality datasets over massive, noisy datasets aligns with the 2026 industry shift toward data-centric AI and efficient fine-tuning of foundation models.
AI Data Prodigy, developed by the architects behind spaCy (Explosion), represents the gold standard in scriptable machine teaching for 2026.
Explore all tools that specialize in label image data. This domain focus ensures AI Data Prodigy (Prodigy by Explosion) delivers optimized results for this specific requirement.
Explore all tools that specialize in named entity recognition. This domain focus ensures AI Data Prodigy (Prodigy by Explosion) delivers optimized results for this specific requirement.
Uses a live model to compute uncertainty scores (entropy) and prioritize the most informative examples for human review.
Integration with OpenAI, Anthropic, or local LLMs to pre-label or explain reasoning for human verification.
Annotation workflows are written in Python, allowing for custom logic, data validation, and UI components.
Simultaneous labeling for text, image, and audio within a single interface for complex cross-domain tasks.
Runs as a local web app; data never leaves your infrastructure unless explicitly configured.
Directly links to spaCy, PyTorch, or Hugging Face for seamless 'label-to-model' iteration.
Deep customization of the frontend annotation interface using web standards.
Install via pip using your unique license key.
Configure your data source (local file, S3, or database).
Select or write a custom Python recipe for your specific task.
Launch the local web server to start the annotation UI.
Connect an initial model to enable active learning suggestions.
Annotate data points flagged by the model's uncertainty score.
Export annotated data in JSONL format for training.
Use the built-in 'train' command to fine-tune your model.
Evaluate model performance and iterate on low-confidence segments.
Deploy the refined model into your production pipeline.
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Verified feedback from other users.
"Highly praised by data scientists for its efficiency and scriptability, though the steep learning curve for Python non-experts is a common note."
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