Use case

Power Generative AI with Synthetic Data

Augment LLM training datasets with Gretel’s synthetic data platform. Generative AI and synthetic data work together to improve model performance and ensure data privacy across the data-centric, LLMOps lifecycle.
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Challenge

The LLM training 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 developer 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

    Model performance can be significantly impacted by issues with data quality, such as missing fields and unwanted bias. These issues can jeopardize the utility of models in production.

  • Data Availability

    Large amounts of cleaned, curated, and annotated data is required to train models. Not only is collecting ground-truth data time-consuming, but it is also expensive.

  • Data Privacy

    Exposing sensitive datasets to public models is dangerous and can risk improper access, memorization, or leakage.

Solution

The LLM training solution

Gretel empowers organizations to accelerate LLM development via safe access to synthetic data. Gretel's synthetic data 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 Retrieval Augmented Generation (RAG), or building your own proprietary foundation model, synthetic data improves performance and ensures safety across the LLMOps lifecycle.

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Key LLM Training Benefits

  • Improve LLM performance

    Multiple, purpose-built synthetic data models for generating high-quality, fully labeled data for more robust ML models.

  • 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

    Privacy is mathematically guaranteed and risks of regulatory fines are mitigated with provably private synthetic data.

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