AI Evals and Analytics

Cohort 4

Cohort 1

Cohort 3

Cohort 2

LLM-as-a-judge is widely used as a low-cost proxy for human or business ground truth, but uncalibrated judge scores can be statistically misleading, even reversing model rankings. This creates real production risk. Eddie Landesberg, an AI Evals researcher, introduces a calibration method to better align LLM-as-a-judge with human judgment and real-world decisions. This deck is from a guest Lightning Lesson by <a href="https://www.linkedin.com/in/eddie-landesberg/" rel="noopener noreferrer" target="_blank">Eddie Landesberg</a>. Check out Lightning Lesson the recording <a href="https://maven.com/p/ed75d8/calibrate-llm-as-a-judge-for-real-world-impact?utm_medium=ll_share_link&amp;utm_source=instructor" rel="noopener noreferrer" target="_blank">here</a>.Also check out Eddie's post "<a href="https://www.cimolabs.com/blog/metrics-lying" rel="noopener noreferrer" target="_blank">Your AI Metrics Are Lying to You</a>".

Calibrate LLM-as-a-judge for Real-world Impact

AI product teams often struggle to decide which metrics actually matter. We introduce AI evaluations and AI product analytics as two distinct but complementary approaches to measuring AI product success. We explain what AI evals measure before and during deployment, how product analytics reflects real-world behavior after launch, and how combining both across the product lifecycle helps teams build AI systems that are reliable, safe, and impactful in production.

AI Evals vs. Product Metrics

The first step in building a solid AI Evals practice is team alignment. Before you run tests or build dashboards, you need to convince leadership and stakeholders that AI Evals matter.Not everyone’s on the same page (yet). Let’s talk about how to have that conversation, and how to influence effectively.

Bring AI Evals to Product Leadership

Product-focused. Immediately executable AI Evals playbook for your product.

A Preview of Our AI Evals Playbook and Framework

Learn how to evaluate, ship, and monitor AI products that perform reliably in production.

Head of AI Applied Science

Stella Liu

Cofounder, AI Evals & Analytics

Amy Chen

Cofounder at AI Evals & Analytics

AI Evals and Analytics Playbook

AI Engineer/ AI Scientist

Staff Machine Learning Engineer

AI product manager

Senior Data Scientist

Data Scientist - Analytics

Shopify

Carvana

Harvard University

System1

Uptake

Seasalt.ai

UCLA

University of Washington

<blockquote>From pre-release testing to in-production monitoring and business impact measurements-Over 85% of AI projects fail to deliver real value or reach production. Building AI in 2026 is easy. Building AI that is reliable, safe, and trusted is not.Evaluation-Driven Development (EDD) is becoming essential. It enables teams to iterate quickly, make informed decisions, ship with confidence, and earn user trust and organizational credibility.But evaluation alone is not enough. AI Product Analytics completes the picture by measuring real-world impact once your AI product meets real users.-🧰 About This Course❌ This is NOT a coding course, because you don’t need one. 🧠 What you need is a mental model shift. 🧭 We guide you through that shift.In this course, we share framework and playbook we use to evaluate and monitor thousands of real-world AI applications.</blockquote>By the end, you’ll have a clear, organization-ready AI evaluation and analytics plan to ship, iterate, and stay accountable in the real world.-AI evals is a fast-moving space. Enrolled students receive first-hand insights and ongoing updates on the latest developments, even after the cohort ends.

Build Effective AI Evals to Drive Product Success

Walk Away with Execution-ready Playbook

Build Your First AI Evals Team

Execute Pre-launch AI Evaluations

Monitor Product and Catch Issues Early

Product LeadersBuilding AI products and need a clear playbook for evaluation frameworks, success metrics, and shipping with confidence.

Data LeadersRedefining team scope and structure in the AI era, aligning evaluation, analytics, and accountability.

Seasoned Data ScientistsLeveling up with AI product skills: learn AI evals, LLM-as-a-judge, and design AI-specific performance metrics.

Collaborative AI Evals with Human Feedback

Rogério Chaves

CTO & Cofounder at Langwatch

Why running evals is a team sport. PMs, data scientists, and SMEs all need a seat at the table, not just the engineers.

Why collaboration is essential to AI evals

Collecting and calibrating human annotations before handing off judgment to automated evaluators at scale.

Human feedback loops before scaling LLM-as-a-judge

Create, review, & iterate on evals without needing to write code or understand technical details

Design evals and simulations for non-technical teammates

Hear customer stories from LangWatch on why cross-functional feedback is key to successful evals

Real-world customer stories from Langwatch

Build Your AI Evals & Analytics Playbook

Head of AI Evals at ASU

Co-Founder at AI Evals & Analytics

It's part of your PRD. AI Evals helps you ship products confidently and safely.

Understand What Is AI Evals and Why It Matters Now

Learn how to define metrics, test model quality, and align stakeholders from Legal to Trust & Safety.

Learn Core AI Evals Framework

Understand how to start your first evals plan and start building with best Evals practice

Get Started with Your Evals Playbook

AI Evals vs. Product Metrics: A 30min Crash Course

Head of Applied Scientist, AI Evals

Learn how to translate between business outcomes and concrete AI metrics

When and why to align AI metrics with product goals

How to use evals pre-release and analytics post-release to guide shipping, monitoring, and iteration

Evals vs. product metrics across product lifecycle

Real-world comparison of metrics for products like ChatGPT vs. your own customer service chatbot

Evals and product metrics for ChatGPT vs. for CX chatbot

How to combine product KPIs and evals dashboards into a simple, repeatable improvement system using a playbook

Build an Evals + Analytics feedback loop

How to Drive AI Evals Adoption

Dr Sebastian Fox

CEO & Co-founder at Composo.ai

AI eval adoption hurdles: from cultural pushback to tough technical trade-offs.

Inside Look at Real-World Evals Challenges

Evals beyond benchmarks: capture user value, ensure consistency, and balance automation with human judgement.

Lessons from Building Evals for LLM Features & Agents

Explores limits of LLM-as-judge methods and scalable AI evaluation alternatives

The Future Beyond “LLM-as-a-Judge”

Evals for Voice AI: Learnings from Google Evals Team

Ravin Kumar

Sr. Researcher at Google Deepmind

Gain practical insights for building, benchmarking, or researching evals in voice and other multimodal contexts

Multimodal Evals Challenges

Learn from practical experience of evals for real-world voice-based conversational AI product like NotebookLM

Design Voice AI Evals

Use evals for real-world conversational quality, setting evals metrics and guardrails in voice systems.

Evaluation Driven Development

Eddie Landesberg

Founder of CIMO Labs

Learn why LLM-as-a-judge is a noisy biased signal rather than ground truth and how to interpret eval results accordingly

A new mental model for LLM-as-a-judge

Learn a calibration-first approach that uses limited human judgment to correct systematic errors in automated evaluators

Calibration as an AI eval design choice

Rethink low-cost eval shortcuts and design eval pipelines that better reflect real-world impact, risk, and decisions

Rethinking “cheap evals” vs. decision risk

Most teams can ship AI fast—but few know how to evaluate it. Learn why AI Evals is now core to product strategy.

Why Product Leadership Needs AI Evals Now

Learn how to bring leadership, product, engineering, and operations together through actionable AI Evals practices.

Build Your First AI Evals Team and Culture

Learn a practical framework to integrate evals into your product quality, performance, safety, and UX.

Design Your AI Evals Playbook

Pragmatic AI Evals: Beyond Vendor Promises

When to consider purchasing an evals tool and how to avoid premature adoption that wastes resources

When and Why to Adopt an AI Evals Vendor

Understand how to use your internal evals playbook, team alignment, and clear metrics to drive tool selection

Design Your AI Evals Playbook First—Then Select Tools

Gain tactics for matching tool capabilities to your project's needs, from pre-release evals to post-production analytics

AI Evals and Analytics

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