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The New Stack and Ops for AI (OpenAI Dev Day 2023): Difference between revisions
The New Stack and Ops for AI (OpenAI Dev Day 2023) (view source)
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|Image = {{#ev:youtube|XGJNo8TpuVA|350}} | |Image = {{#ev:youtube|XGJNo8TpuVA|350}} | ||
|Name = The New Stack and Ops for AI | |Name = The New Stack and Ops for AI | ||
|Type = | |Type = Technical | ||
|Event = OpenAI Dev Day 2023 | |Event = OpenAI Dev Day 2023 | ||
|Organization = OpenAI | |Organization = OpenAI | ||
|Channel = OpenAI | |||
|Presenter = Shyamal Hitesh Anadkat, Sherwin Wu | |Presenter = Shyamal Hitesh Anadkat, Sherwin Wu | ||
|Description = A new framework to navigate the unique considerations for scaling non-deterministic apps from prototype to production. | |Description = A new framework to navigate the unique considerations for scaling non-deterministic apps from prototype to production. | ||
| | |Date = Nov 14, 2023 | ||
|Website = https://www.youtube.com/watch?v=XGJNo8TpuVA | |Website = https://www.youtube.com/watch?v=XGJNo8TpuVA | ||
}} | }} | ||
== Introduction == | == Introduction == | ||
"[[Stack and Ops for AI]]" is a comprehensive guide focusing on the transition of [[AI]] applications from prototype to production. This page synthesizes a presentation by [[Sherwin]] and [[Shyamal]] from [[OpenAI]], providing insights into the journey of [[ChatGPT]] and [[GPT-4]], their integration into various products, and the development process that transforms a simple prototype into a scalable, production-ready tool. | |||
== Background: The Rise of GPT == | == Background: The Rise of GPT == | ||
=== ChatGPT and GPT-4: A Brief History === | === ChatGPT and GPT-4: A Brief History === | ||
[[ChatGPT]], launched in late November 2022, and [[GPT-4]], introduced in March 2023, mark significant milestones in [[AI development]]. These models transitioned from novel experiments to integral parts of daily life and work, providing a foundation for developers to innovate and integrate AI into diverse products. | |||
== From Prototype to Production: A Framework == | == From Prototype to Production: A Framework == | ||
The process of scaling non-deterministic applications like [[GPT models]] involves a structured framework, addressing challenges like model inconsistency, scaling, and [[user experience]]. This framework is essential for transitioning prototypes into reliable, production-level applications. | |||
=== Building a Delightful User Experience === | === Building a Delightful User Experience === | ||
The [[user experience]] is pivotal, especially given the unique interaction challenges of AI models. Strategies include controlling uncertainty, building guardrails for steerability and safety, and designing a user-centered interface that enhances and augments human capabilities. | |||
=== Managing Model Consistency === | === Managing Model Consistency === | ||
[[Model consistency]] is crucial as applications scale. This involves constraining model behavior and grounding the model with a [[knowledge store]] or tools. Features like [[JSON mode]] and reproducible outputs using the 'C' parameter help achieve this consistency. | |||
=== Grounding the Model === | === Grounding the Model === | ||
Grounding models with real-world knowledge reduces hallucinations and improves response accuracy. This can be implemented through various methods, such as [[vector databases]] or integrating external [[APIs]] to provide up-to-date information. | |||
== Evaluating and Improving Application Performance == | == Evaluating and Improving Application Performance == | ||
Evaluations play a key role in refining and ensuring the consistent performance of AI applications. Strategies include creating [[evaluation suites]] tailored to specific use cases, using automated evaluations, and leveraging model-graded evaluations. | |||
=== Evaluation-Driven Development === | === Evaluation-Driven Development === | ||
Adopting an [[evaluation-driven development]] approach ensures that applications meet user expectations and maintain high-quality standards. This involves tracking evaluation runs, using GPT models for grading, and focusing on custom metrics relevant to specific applications. | |||
== Orchestrating for Scale: Managing Latency and Cost == | == Orchestrating for Scale: Managing Latency and Cost == | ||
As applications gain popularity, managing scale becomes critical. Strategies for managing [[latency]] and [[costs]] include semantic caching, routing to cheaper models, and fine-tuning to optimize performance without compromising user experience. | |||
== LLM Ops: A New Discipline in AI Development == | == LLM Ops: A New Discipline in AI Development == | ||
Large Language Model Operations (LLM Ops) is an emerging discipline focused on the operational management of LLMs. It encompasses practices and infrastructure for monitoring, optimizing performance, ensuring security and compliance, and facilitating collaboration between teams. LLM Ops is crucial for scaling applications to meet the demands of a growing user base. | |||
[[Large Language Model Operations]] (LLM Ops) is an emerging discipline focused on the operational management of LLMs. It encompasses practices and infrastructure for monitoring, optimizing performance, ensuring security and compliance, and facilitating collaboration between teams. LLM Ops is crucial for scaling applications to meet the demands of a growing user base. | |||
==Comments== | ==Comments== | ||
<comments /> | <comments /> | ||