OpenAI - OpenAI DevDay 2024 | Balancing accuracy, latency, and cost at scale
The speakers, Colin Jarvis and Jeff Harris from OpenAI, discuss the challenges of scaling AI applications as user bases grow. They emphasize the importance of optimizing for accuracy first, using the most intelligent models until accuracy targets are met. Once accuracy is achieved, the focus shifts to optimizing latency and cost. They provide practical techniques such as prompt engineering, retrieval-augmented generation (RAG), and fine-tuning to improve accuracy. For latency, they suggest breaking down total request latency into network latency, time to first token, and time between tokens, and offer strategies to optimize each. Cost-saving measures include using prompt caching and the BatchAPI, which offers significant discounts for asynchronous processing. The speakers highlight the importance of balancing these factors to build efficient and effective AI applications.
Key Points:
- Start by optimizing for accuracy using intelligent models until targets are met.
- Once accuracy is achieved, focus on reducing latency and cost.
- Use techniques like prompt engineering, RAG, and fine-tuning for accuracy.
- Optimize latency by managing network latency, time to first token, and time between tokens.
- Reduce costs with prompt caching and BatchAPI for asynchronous processing.
Details:
1. 🚀 Scaling Challenges and Strategies
1.1. Rapid User Base Expansion
1.2. Sustainable Scaling
1.3. Critical Decision Making
1.4. Maintaining Performance
2. 📈 Optimization and Cost Reduction
- Optimization involves multiple techniques and trade-offs, with no single playbook applicable to all scenarios.
- The session provides approaches and best practices for effective optimization.
- Central to OpenAI's mission is the optimization of applications, aiming for more intelligent and faster models.
- GPT-4o demonstrates significant improvements, being twice as fast as 4 Turbo, showcasing advancements in speed and efficiency.
- OpenAI is dedicated to continuous cost reduction, emphasizing the importance of making AI more accessible and efficient.
3. 💡 Model Improvements and Use Cases
3.1. Cost Reduction and Efficiency
3.2. New Use Cases and Increased Consumption
3.3. Decision-Making and Model Selection
4. 🔍 Accuracy Optimization Techniques
- Balancing accuracy, latency, and costs is crucial in AI applications to maintain accuracy at the lowest cost and speed.
- The approach involves starting with optimizing for accuracy using the most intelligent model until the accuracy target is met.
- An accuracy target should have business significance, such as correctly routing 90% of customer service tickets on the first attempt.
- Once the accuracy target is achieved, the focus shifts to optimizing for latency and cost while maintaining accuracy.
- Establishing a minimum accuracy target is essential for delivering ROI and avoiding debates on what constitutes sufficient accuracy for production.
- Case Study: A company improved customer service efficiency by 30% by setting a 90% accuracy target for ticket routing, then optimizing for cost and latency.
- Strategy: Use a tiered model approach, starting with a high-accuracy model and transitioning to more cost-effective models once the target is met.