The New Stack and Ops for AI

TL;DR

Learn how to build a user-friendly AI application, handle model inconsistency, iterate with evaluations, and manage scale using orchestration.

Transcript

[music] -Hi, everyone. Welcome to the new Stack and Ops for AI, going from prototype to production. My name is Sherwin, and I lead the Engineering team for the OpenAI Developer Platform, the team that builds and maintains the APIs that over 2 million developers, including hopefully many of you, have used to build products on top of our models... Read More

Key Insights

• 🌍 It's only been less than a year since ChatGPT was launched, but it has quickly transformed into an essential tool for enterprises, startups, and developers worldwide.
• 🚀 Building a prototype with a language model like ChatGPT is easy, but transitioning from prototype to production can be challenging due to the non-deterministic nature of these models.
• 💡 To create a delightful user experience, it's important to control for uncertainty and build guardrails for steerability and safety. This can be done by allowing user iteration, providing feedback controls, and communicating the system's capabilities and limitations.
• 😎 Evaluations are crucial for testing and iterating on your application, and automated evals can help monitor progress, test for regressions, and establish a solid foundation of trust in your model's performance.
• 🔍 Model consistency can be improved by constraining the model's behavior and grounding it with additional real-world knowledge. JSON mode and reproducible outputs using the C parameter are two new model-level features that help manage inconsistency.
• 🔬 Developing evaluation suites and implementing model-graded evals can provide systematic processes to evaluate the performance of your models and prevent regressions. Automated evals can reduce human involvement and speed up the evaluation process.
• ⚙️ Scaling applications requires managing costs and latency. Strategies like semantic caching and routing to cheaper models, such as fine-tuned 3.5 Turbo, can help optimize performance and reduce expenses.
• 🏭 Large Language Model Operations (LLM Ops) is an emerging field focused on the operational management of LLMs. It encompasses practices, tools, and infrastructure that address challenges like monitoring, security compliance, data management, and development velocity.

Questions & Answers

Q: How can developers control uncertainty and enhance the user experience in AI applications?

Developers can control uncertainty by keeping the human in the loop to iterate and improve the quality of AI-generated outputs over time. Providing feedback controls and communicating system capabilities and limitations to users also contribute to a more transparent and user-centric experience.

Q: What are some strategies for managing model inconsistency in AI applications?

Two strategies for managing model inconsistency include constraining the model's behavior itself and grounding the model with real-world knowledge. By constraining model behavior at the model level, developers can enforce output within specific constraints or formats. Grounding the model involves providing additional facts or context to reduce the likelihood of incorrect or hallucinated information.

Q: How can developers ensure the performance and reliability of AI applications?

Developers can ensure performance and reliability by implementing evaluations that measure the application's performance on real-world scenarios. Creating evaluation suites specific to the application's use cases and metrics helps test for regressions and validate improvements over time. Automated evaluations, including model-graded evals, can also assist in monitoring progress and testing for regressions quickly.

Q: How can developers manage scale and optimize costs in AI applications?

Semantic caching can be implemented to reduce the number of round trips to the API by leveraging caching mechanisms that store previous responses to similar queries. Additionally, routing to cheaper models, such as fine-tuned versions of lower-cost models, can help reduce costs while still delivering acceptable performance. By carefully managing latency and cost considerations, developers can scale their AI applications effectively.

Summary & Key Takeaways

• Building a user-centric AI application involves controlling uncertainty and implementing guardrails to enhance user experience.

• Managing model inconsistency by constraining model behavior and grounding the model with real-world knowledge helps deliver consistent results.

• Evaluating performance through manual and automated evaluations ensures the application meets user expectations.

• Managing scale involves semantic caching and routing to cheaper models to optimize costs and reduce latency.