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(Created page with "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 th...")
 
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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.
{{Presentation infobox
|Image = {{#ev:youtube|XGJNo8TpuVA|350}}
|Name = The New Stack and Ops for AI
|Type = Technical
|Event = OpenAI Dev Day 2023
|Organization = OpenAI
|Channel = OpenAI
|Presenter = Shyamal Hitesh Anadkat, Sherwin Wu
|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
}}


== Introduction ==
== 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 ==
"[[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.
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.
== Background: The Rise of GPT ==


== Framework for Scaling AI Applications ==
=== ChatGPT and GPT-4: A Brief History ===
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 ===
[[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.
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.
== From Prototype to Production: A Framework ==


=== Model Consistency ===
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.
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.
=== Building a Delightful User Experience ===


=== Evaluating AI Model Performance ===
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.
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 Model Consistency ===


=== Managing Scale: Orchestration ===
[[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.
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.
=== Grounding the Model ===


== Large Language Model Operations (LLM Ops) ==
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.
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.
== 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 ===
 
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 ==
 
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 ==
 
[[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.
 
 
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