Snorkel GenFlow

Snorkel GenFlow offers programmatic curation, annotation, and management of instruction datasets for generative AI use cases.
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Mastering GenAI: the importance of high-quality data

The key to the superior performance of generative models like ChatGPT is the quality of the “instruction and response” data they are trained on. However, creating and curating these datasets remains a largely ad hoc, manual, and costly process. These data-centric operations are often relegated to second-tier status in the core AI development process, have lengthy review cycles, and are less than ideal for teams working with private, expertise-intensive data.


Obtain the optimal mix of prompts and/or responses for the benchmarks and tasks crucial to your deployment setting.


Leverage model-driven approaches to filter for high-quality data, the cornerstone of top-tier AI.


Combine programmatic and human-driven resources to create high-quality responses, facilitated by algorithmically-driven routing, review, and modeling techniques.

Making generative AI data operations first-class and programmatic

Use Cases



Enhance chatbot interactions with Snorkel GenFlow by streamlining the creation and management of high-quality instruction datasets.


Utilize a combination of programmatic and human-driven resources to curate high-quality Q&A data, effectively improving the model's ability to provide accurate and insightful answers.


Efficiently curate and manage datasets for creating AI models that summarize articles, blog posts, books, and documents with precision on your infrastructure.

Stay up to date on the latest research

Pioneering AI research is part of our DNA—that’s why in addition to keeping up with the latest research, we also regularly publish. Here are a few of the recent papers in this area relevant to the data-centric side of building high-performing generative AI models:

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Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
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The Future of Data-Centric AI

June 7-8, 2023

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