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How to Optimize AI Product Latency when the model is Fast

Hosted by Mahesh Yadav

Fri, Oct 9, 2026

4:00 PM UTC (45 minutes)

Virtual (Zoom)

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Master Agentic AI for PMs with Official Anthropic Claude Certifications
Mahesh Yadav
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What you'll learn

Select the Right Model: Balancing Speed, Cost & Accuracy

Compare Opus, Sonnet, Haiku. Understand latency-cost-accuracy tradeoffs. Choose models by use case, not just capability.

Evaluate Gen AI Applications With Production Metrics

Build evaluation frameworks. Measure latency, accuracy, cost, user satisfaction. Make data-driven deployment decisions.

Design Concurrent Systems & Reduce Queue Time

Master queueing design. Batch requests efficiently. Handle concurrent users. Eliminate time that hides model speed.

Stream Responses & Optimize Frontend Performance

Master response streaming & time-to-first-token optimization. Design frontend for incremental responses. Feel 5x faster.

Why this topic matters

Latency optimization separates mid-level engineers from senior engineers. Few engineers understand how to optimize latency across the full stack: model selection, evaluation frameworks, system architecture, & user experience. Companies pay premiums for engineers who can diagnose why a system feels slow and fix it. Master this topic, & you unlock senior roles at frontier AI companies building products millions use daily.

You'll learn from

Mahesh Yadav

Ex AI Product Lead - Google l Meta l Microsoft l AWS | 10k+ Alums

Mahesh Yadav brings 20+ years of experience building AI products at Google, Meta, AWS, Microsoft. He holds 12 patents in AI training, power management and computer vision. He has launched major agentic-AI initiatives (for example launching an agent for AWS Bedrock, featured in CEO keynote) and trained thousands of professionals to succeed in AI roles. With this programme you get rare access to CEO-level of mentorship.
Substack AI PM Newsletter l Linkedin Community of AI PMs l YouTube For Free Sessions Recordings
Currently, he is building back-office AI agents for the enterprise, starting with in-house legal teams through LegalGraph.AI. His work bridges education and real-world AI deployment, helping organizations adopt agentic systems that automate complex knowledge work.

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