The New Stack and Ops for AI (OpenAI Dev Day 2023)

The Stack and Ops for AI presentation, delivered by Sherwin, leader of the OpenAI Developer Platform Engineering team, and Shyamal of the Applied team, offers a comprehensive framework for transitioning AI applications from prototype to production. This talk is significant in the rapidly evolving field of artificial intelligence, particularly focusing on the deployment and scaling of applications built on models like ChatGPT and GPT-4.

Introduction

Sherwin sets the stage by reflecting on the rapid impact of ChatGPT since its launch in November 2022 and GPT-4 in March 2023. He emphasizes the transition of GPT from a social media novelty to a powerful tool integrated into products by enterprises, startups, and developers.

Building a Prototype with AI Models

The initial focus of AI development often centers on creating a prototype, which is relatively straightforward with OpenAI models. However, the real challenge emerges in moving these prototypes into production, primarily due to the non-deterministic nature of models like GPT-4, complicating scalability.

Framework for Scaling AI Applications

The presentation introduces a structured framework to guide developers in scaling their AI applications. This framework comprises several layers, each addressing key challenges in AI deployment:

User Experience Design

Shyamal discusses the importance of crafting user experiences that account for the probabilistic nature of AI models. Strategies include managing uncertainty, building user-centric interfaces, and establishing clear communication about the AI's capabilities and limitations.

Model Consistency

Sherwin elaborates on strategies for ensuring model consistency. This includes constraining model behavior at the model level, and grounding the model with real-world knowledge, such as using a knowledge store or tools to reduce hallucinations and improve response accuracy.

Evaluating AI Model Performance

A crucial step in AI deployment is evaluating model performance. Shyamal suggests creating evaluation suites tailored to specific use cases and adopting automated evaluations to monitor progress and detect regressions.

Managing Scale: Orchestration

Sherwin addresses the challenges of scaling AI applications, focusing on managing latency and cost. Strategies include semantic caching to reduce API calls and routing to cheaper models like GPT-3.5 Turbo, potentially fine-tuned for specific use cases.

Large Language Model Operations (LLM Ops)

The concept of LLM Ops emerges as a critical discipline for managing the operational aspects of large language models. This includes monitoring, security, data management, and optimizing performance. LLM Ops is likened to DevOps, marking a new era in AI application development and deployment.