Use case

Power Generative AI with Synthetic Data

Augment LLM training datasets with Gretel to improve performance and ensure safety across the LLMOps lifecycle.
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Challenge

The data challenge

Large Language Models (LLMs) are trained extensively on the vast amount of publicly available data. Further extracting value from these models involves additional training on new or private data. This 'last mile' training presents AI teams with challenges related to data privacy, quality, and availability. These hurdles are common to both enterprises looking to adapt LLMs for domain-specific tasks as well as frontier AI teams building their own foundation models.

  • Data Quality

    Issues with data quality such as missing fields and unwanted bias greatly impact model performance, jeopardizing the utility of models in production.

  • Data Availability

    Training models requires large amounts of cleaned, curated and annotated data. Collecting ground-truth data is time-consuming and expensive.

  • Data Privacy

    Exposing sensitive datasets to public models is akin to placing them on the public cloud, risking improper access, memorization, or leakage.

Solution

The Gretel solution

Gretel empowers organizations to accelerate LLM development via safe access to synthetic data. Gretel's platform provides the end-to-end capabilities for generating, evaluating, and operationalizing synthetic data for LLM training at scale. Whether fine-tuning a LLM, implementing RAG, or building your own  proprietary foundation model, synthetic data improves performance and ensures safety across the LLMOps lifecycle.

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Key Benefits

  • Improve LLM performance

    Multiple synthetic data models purpose-built for producing high-quality and fully labeled data for more robust LLMs.

  • Faster time to value

    Accelerate generative AI applications with on-demand access to training data that embeds directly in your LLM training workflows.

  • Safe ML training

    Mathematically guaranteed privacy and mitigated risks of regulatory fines with provably private synthetic data.

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