Thoughts about OpenAI’s new Distillation feature

OpenAI has recently introduced a compelling new distillation feature, setting itself apart from traditional methods. Unlike Synthetic Data Distillation—on which I wrote previously and coded a ⁠raft-distillation-recipe repository Github repository—this approach leverages Production Data driven Distillation. This innovative method harnesses real-time production data, offering a dynamic and practical alternative for optimizing AI models.

What is it made of?

From their ⁠blog post:

• Stored Completions: Developers can now easily generate datasets for distillation by automatically capturing and storing the input-output pairs generated by one of our models, like GPT-4o or o1-preview through our API. With Stored Completions, you can easily build datasets with your production data to evaluate and fine-tune models. Developers can review this integration guide(opens in a new window) to learn how to opt-in to storing completions.
• Evals (beta): Developers can now create and run custom evaluations on our platform to measure model performance on specific tasks. Instead of manually creating evaluation scripts and integrating disparate logging tools, Evals provides an integrated way to measure model performance. You can either use data from Stored Completions or upload existing datasets to set up your evaluations. Evals can also be used independently of fine-tuning to quantitatively evaluate model performance for your use cases.
• Fine-tuning: Stored Completions and Evals are fully integrated with our existing fine-tuning offering. This means that developers can use datasets created with Stored Completions in their fine-tuning jobs and run evaluations on fine-tuned models using Evals, all within our platform.

What makes it compelling?

What sets this feature apart is the ability to automatically and continuously collect live production data, which can then be used to train, evaluate, and deploy a smaller model that meets specific quality and cost thresholds. This end-to-end solution for LLM operations (LLM Ops) offers an efficient, streamlined approach, making it highly attractive to many companies looking to optimize their AI deployment. By minimizing manual intervention and leveraging real-world data, businesses can swiftly adapt their AI models to meet evolving performance and budgetary requirements.

Why is it important?

Utilizing Stored Completions and Evals in conjunction with fine-tuning presents a significant opportunity for businesses to cut down on the operational costs associated with deploying large language models (LLMs). High-performance LLMs, such as GPT-4 or o1-preview, can be expensive due to their substantial computational and resource demands. This innovative distillation method allows companies to continually retrain smaller, cost-effective models using real-time production data, thereby emulating the performance of their larger versions.

The seamless integration of automated data collection from actual usage (Stored Completions) and customized performance assessments (Evals) reduces the dependency on labor-intensive data curation and validation. This streamlined process enables businesses to shift away from expensive models while preserving high quality and relevance. Essentially, this approach not only slashes costs but does so with minimal setup and ongoing operational expenses, offering an efficient pathway for businesses to optimize their AI investments and achieve substantial savings.

Potential Challenges

While Production Data driven Distillation offers numerous advantages, it is not without its drawbacks:

1. Dependence on Production Traffic: The efficacy of this approach is inherently tied to the volume and diversity of production traffic. This limitation means that the distilled model may be biased towards the types of interactions and queries historically logged, potentially overlooking edge cases or new types of user questions.

2. Adaptation to Specialized Settings: The integration with Retrieval-Augmented Generation (RAG) settings remains a complex issue. In these scenarios, the model’s output is highly contingent on user-specific contexts and dynamically retrieved information, raising the question of how well a distilled model can adapt and maintain accuracy in such fluid conditions.

3. Ongoing Performance Enhancement: Once the distilled model replaces the larger one in the production environment, maintaining and improving its performance becomes critical. Implementing A/B testing between the large and small models can facilitate the continued collection of live data for model refinement. However, this process places a significant operational burden on developers and may dilute the anticipated cost savings due to the overhead of managing simultaneous models.

4. Privacy Concerns: Data privacy and regulatory compliance pose significant challenges, especially in regions with stringent data protection laws such as the European Union with its General Data Protection Regulation (GDPR) and California with the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA). In such markets, Synthetic Data Distillation may still be preferred to avoid potential legal implications and privacy breaches, thus complicating the global applicability of Production Data driven Distillation.

5. Data Quality and Noise: Filtering out noise and ensuring high-quality data from production traffic is another daunting task. The presence of outliers, irrelevant interactions, and non-representative samples can hinder the model training process, leading to suboptimal model performance. The Evals framework can help evaluate the answers and be used to filter out irrelevant or subpar quality answers from the training dataset, but this requires careful integration.

6. User Acceptance: Users may exhibit resistance to changes in model outputs, especially if they perceive a decline in performance. Managing user expectations and ensuring a smooth transition while maintaining high satisfaction levels is crucial for the effective deployment of distilled models.

7. Lack of API for Evals Feature: The Evals feature currently appears to lack an API, limiting its usage to the user interface. Automated or programmatic access to evaluations is not supported. Developers accustomed to scripting and integrating features via APIs might find this restrictive and less efficient for complex or bespoke workflows.

Addressing these challenges is essential to maximizing the benefits of Production Data driven Distillation and ensuring its long-term viability as a cost-effective solution for AI model optimization.

Conclusion

As we look towards the future of AI model optimization, the integration of automated data collection and evaluation mechanisms like Stored Completions and Evals offers a path towards more efficient and cost-effective AI deployments.

Businesses seeking more flexible evaluation tools should note that Azure AI provides an API for evaluation, which could offer more flexibility and integration options.

For those interested in synthetic dataset generation, my previous blog post on Synthetic Data Distillation offers valuable insights and can be an excellent resource for learning and implementation.

I would love to hear your thoughts on this new feature. Do you see yourself leveraging Production Data driven Distillation, or do you prefer the traditional approach of Synthetic Data Distillation? How do you feel about the privacy challenges associated with using real production data in model training? Your feedback and perspectives are invaluable, so please share your opinions in the comments below!