Event Recap - Crossing the AI Prototype Chasm
General
Matt Wyman
,
December 10, 2024
Download the presentation deck here.
AICamp San Francisco hosted a talk at the GitHub Community Space, where Matt Wyman, co-founder and CEO of Okareo, delved into the challenges and solutions surrounding LLM development. The core message highlighted the critical role of behavioral evaluation, drawing parallels with Behavior-Driven Development (BDD), in navigating the path from prototype to production.
The AI landscape is evolving rapidly, with new patterns and applications emerging daily. While reported successes vary widely, the significant increase in GPU spending indicates a surge in experimentation. However, a considerable gap exists between prototype and production, driven by factors like stability, cost, performance, and compliance.
To bridge this chasm, we must shift our focus to behavioral evaluation. Traditional "gated" or "yolo" approaches fall short. Instead, we need to adopt a scientific mindset, building like developers and providing feedback like subject matter experts.
Behavioral evaluation aligns closely with the principles of BDD, where the focus is on defining and testing the desired behavior of the system. In the context of LLMs, this translates to evaluating the model's behavior through task-specific metrics, providing insights into issues like hallucination and errors. However, where BDD is about testing, behavioral evaluation is a continuous practice in development and production.
By investing in continuous evaluation during development and production, developers gain valuable insights into model behavior, leading to improved development cycles, enhanced performance, and greater business clarity. This iterative process, akin to the BDD cycle of defining, developing, and testing, ensures that the LLM application meets the desired requirements and delivers real-world value. The approach also accelerates development from experimentation to production by providing early clarity on gaps and rapid paths to resolution when there are issues.
Ultimately, the key to successful deployment of LLM applications is in understanding and measuring their behavior. By prioritizing evaluation and monitoring, we can unlock the true potential of AI and pave the way for more innovative and impactful solutions. You don’t need your deployment blocked by a financial contact center agent discussing Avocado Smoothies.
Download the presentation deck here.
Download the presentation deck here.
Download the presentation deck here.
AICamp San Francisco hosted a talk at the GitHub Community Space, where Matt Wyman, co-founder and CEO of Okareo, delved into the challenges and solutions surrounding LLM development. The core message highlighted the critical role of behavioral evaluation, drawing parallels with Behavior-Driven Development (BDD), in navigating the path from prototype to production.
The AI landscape is evolving rapidly, with new patterns and applications emerging daily. While reported successes vary widely, the significant increase in GPU spending indicates a surge in experimentation. However, a considerable gap exists between prototype and production, driven by factors like stability, cost, performance, and compliance.
To bridge this chasm, we must shift our focus to behavioral evaluation. Traditional "gated" or "yolo" approaches fall short. Instead, we need to adopt a scientific mindset, building like developers and providing feedback like subject matter experts.
Behavioral evaluation aligns closely with the principles of BDD, where the focus is on defining and testing the desired behavior of the system. In the context of LLMs, this translates to evaluating the model's behavior through task-specific metrics, providing insights into issues like hallucination and errors. However, where BDD is about testing, behavioral evaluation is a continuous practice in development and production.
By investing in continuous evaluation during development and production, developers gain valuable insights into model behavior, leading to improved development cycles, enhanced performance, and greater business clarity. This iterative process, akin to the BDD cycle of defining, developing, and testing, ensures that the LLM application meets the desired requirements and delivers real-world value. The approach also accelerates development from experimentation to production by providing early clarity on gaps and rapid paths to resolution when there are issues.
Ultimately, the key to successful deployment of LLM applications is in understanding and measuring their behavior. By prioritizing evaluation and monitoring, we can unlock the true potential of AI and pave the way for more innovative and impactful solutions. You don’t need your deployment blocked by a financial contact center agent discussing Avocado Smoothies.
Download the presentation deck here.
Download the presentation deck here.
Download the presentation deck here.
AICamp San Francisco hosted a talk at the GitHub Community Space, where Matt Wyman, co-founder and CEO of Okareo, delved into the challenges and solutions surrounding LLM development. The core message highlighted the critical role of behavioral evaluation, drawing parallels with Behavior-Driven Development (BDD), in navigating the path from prototype to production.
The AI landscape is evolving rapidly, with new patterns and applications emerging daily. While reported successes vary widely, the significant increase in GPU spending indicates a surge in experimentation. However, a considerable gap exists between prototype and production, driven by factors like stability, cost, performance, and compliance.
To bridge this chasm, we must shift our focus to behavioral evaluation. Traditional "gated" or "yolo" approaches fall short. Instead, we need to adopt a scientific mindset, building like developers and providing feedback like subject matter experts.
Behavioral evaluation aligns closely with the principles of BDD, where the focus is on defining and testing the desired behavior of the system. In the context of LLMs, this translates to evaluating the model's behavior through task-specific metrics, providing insights into issues like hallucination and errors. However, where BDD is about testing, behavioral evaluation is a continuous practice in development and production.
By investing in continuous evaluation during development and production, developers gain valuable insights into model behavior, leading to improved development cycles, enhanced performance, and greater business clarity. This iterative process, akin to the BDD cycle of defining, developing, and testing, ensures that the LLM application meets the desired requirements and delivers real-world value. The approach also accelerates development from experimentation to production by providing early clarity on gaps and rapid paths to resolution when there are issues.
Ultimately, the key to successful deployment of LLM applications is in understanding and measuring their behavior. By prioritizing evaluation and monitoring, we can unlock the true potential of AI and pave the way for more innovative and impactful solutions. You don’t need your deployment blocked by a financial contact center agent discussing Avocado Smoothies.
Download the presentation deck here.
Download the presentation deck here.
Download the presentation deck here.
AICamp San Francisco hosted a talk at the GitHub Community Space, where Matt Wyman, co-founder and CEO of Okareo, delved into the challenges and solutions surrounding LLM development. The core message highlighted the critical role of behavioral evaluation, drawing parallels with Behavior-Driven Development (BDD), in navigating the path from prototype to production.
The AI landscape is evolving rapidly, with new patterns and applications emerging daily. While reported successes vary widely, the significant increase in GPU spending indicates a surge in experimentation. However, a considerable gap exists between prototype and production, driven by factors like stability, cost, performance, and compliance.
To bridge this chasm, we must shift our focus to behavioral evaluation. Traditional "gated" or "yolo" approaches fall short. Instead, we need to adopt a scientific mindset, building like developers and providing feedback like subject matter experts.
Behavioral evaluation aligns closely with the principles of BDD, where the focus is on defining and testing the desired behavior of the system. In the context of LLMs, this translates to evaluating the model's behavior through task-specific metrics, providing insights into issues like hallucination and errors. However, where BDD is about testing, behavioral evaluation is a continuous practice in development and production.
By investing in continuous evaluation during development and production, developers gain valuable insights into model behavior, leading to improved development cycles, enhanced performance, and greater business clarity. This iterative process, akin to the BDD cycle of defining, developing, and testing, ensures that the LLM application meets the desired requirements and delivers real-world value. The approach also accelerates development from experimentation to production by providing early clarity on gaps and rapid paths to resolution when there are issues.
Ultimately, the key to successful deployment of LLM applications is in understanding and measuring their behavior. By prioritizing evaluation and monitoring, we can unlock the true potential of AI and pave the way for more innovative and impactful solutions. You don’t need your deployment blocked by a financial contact center agent discussing Avocado Smoothies.
Download the presentation deck here.
Download the presentation deck here.
