Boris Selitser, Co-Founder of Okareo, discusses the power of synthetic data and agent evaluations in LLM development. Topics include safeguarding AI products, online evaluation in agent systems, custom evaluations, and the role of synthetic data in AI development. The conversation also explores agent architectures, challenges faced by startups, and the potential of agent frameworks in the tech industry.
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Quick takeaways
Synthetic data enhances model behavior description and robustness in LLM applications.
Developers are transitioning to data-driven development skills akin to software engineering cycles.
Custom evaluation metrics tailored to specific applications are crucial for effective assessment.
Deep dives
Importance of Evaluation in AI Agents
When building AI agents, proper evaluation, especially using synthetic data, plays a crucial role in measuring their effectiveness and performance. Understanding the power of choosing the right metrics, continuously iterating on them, and tracking their relevance is highlighted. Evaluation metrics are essential for assessing output quality and system behaviors, emphasizing the need for constant metric updates.
Evolution of Working Cycles in AI Model Development
The development cycles for foundational AI models are becoming shorter and more aligned with software development practices. Prompts and models are iterated upon more rapidly, demonstrating similarities to software engineering cycles. Software engineers are shifting towards data-driven development skills and transitioning into the realm of building systems with foundational models.
Value of Custom Evaluation Metrics in AI Applications
Utilizing custom evaluation metrics tailored to specific applications is emphasized over generic benchmarks. Identifying practical metrics that align with end-user or business value is crucial for effective evaluation. The importance of continuous iteration and customization of metrics based on evolving needs and features is highlighted.
Challenges and Strategies in Agent Architecture Development
Building agent architectures presents challenges such as tailoring solutions to individual company needs. The complexity of integrating agents across diverse tasks and domains requires thoughtful design patterns and continuous experimentation. Domain specificity, like focusing on customer onboarding for tech companies, may offer a more viable approach to developing agent frameworks.
Exploring State Management Solutions in AI Architectures
The discussion touches on potential solutions like graph-based state management in agent designs. While graph-based concepts offer comprehensive information flow, a simpler state machine approach might be more accessible for initial adoption. The balancing act between complexity and usability in AI architecture design is under examination for industry adoption.
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Navigating the AI Frontier: The Power of Synthetic Data and Agent Evaluations in LLM Development // MLOps podcast #241 with Boris Selitser, Co-Founder and CTO/CPO of Okareo.
A big thank you to LatticeFlow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/
// Abstract
Explore the evolving landscape of building LLM applications, focusing on the critical roles of synthetic data and agent evaluations. Discover how synthetic data enhances model behavior description, prototyping, testing, and fine-tuning, driving robustness in LLM applications. Learn about the latest methods for evaluating complex agent-based systems, including RAG-based evaluations, dialog-level assessments, simulated user interactions, and adversarial models. This talk delves into the specific challenges developers face and the tradeoffs involved in each evaluation approach, providing practical insights for effective AI development.
// Bio
Boris is the Co-Founder and CTO/CPO at Okareo. Okareo is a full-cycle platform for developers to evaluate and customize AI/LLM applications. Before Okareo, Boris was Director of Product at Meta/Facebook, leading teams building internal platforms and ML products. Examples include a copyright classification system across the Facebook apps and an engagement platform for over 200K developers, 500K+ creators, and 12M+ Oculus users. Boris has a bachelor’s in Computer Science from UC Berkeley.
// MLOps Jobs board
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https://mlops-community.myshopify.com/
// Related Links
https://docs.okareo.com/blog/data_loophttps://docs.okareo.com/blog/agent_eval
The Real E2E RAG Stack // Sam Bean // MLOps Podcast #217 - https://youtu.be/8uZst7pgOw0
Timestamps:
[00:00] Boris' preferred coffee
[00:37] Takeaways
[02:32] Please like, share, leave a review, and subscribe to our MLOps channels!
[02:48] Software Engineering and Data Science
[06:01] AI Transformative Potential Explained
[10:31] Prompt Injection Protection Strategies
[17:03] Agent's metrics for Jira
[24:11] Data and Metrics Evolution
[27:54] Evaluation Focus Enhances Systems
[31:22 - 32:52] LatticeFlow AD
[32:55] Custom Evaluation and Synthetic Data
[36:23] Synthetic data for expansion, evaluation, and map
[41:06] Diverse agents' personalities for readiness
[44:25] Agent functions
[46:17] Optimizing Routing Agents
[50:04] Adapting to tool output for decision-making
[52:56] Agent framework evolution
[55:41] Agent framework for delivering value
[57:03] Wrap up
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