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Master Evaluation Techniques for LLM Apps

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Haroon Choudery

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What you'll learn

Why evaluations are necessary

Learn what role evaluations play in LLM apps and why they are crucial ensuring their effectiveness & reliability.

How to choose the right evaluations

Explore how to select the best evaluation techniques (i.e., rules-based evals or LLM judges) for your LLM use case

Improving evaluation reliability

Discover methods, like fine-tuning & expert alignment, for improving the consistency and accuracy of your evaluations.

Why this topic matters

Evaluating LLM applications is crucial for AI teams to ensure the effectiveness and reliability of AI systems. Mastering evaluation techniques helps AI teams increase development speed, drive better business outcomes, and maintain a competitive edge. An ureliable LLM app can lead to poor decision-making, user dissatisfaction, and potential security vulnerabilities.

You'll learn from

Haroon Choudery

CEO, Autoblocks AI

Haroon is the Co-Founder & CEO at Autoblocks AI, one of the first-ever AI evaluation platforms.

He is also the host of the Building With AI podcast, where he interviews & distills best practices from top AI product teams at companies like OpenAI, Intercom, and Retool.

Haroon previously founded one of the top AI literacy nonprofits in the US, AI For Anyone, and is passionate about democratizing knowledge about AI.

Based on best practices from AI teams at


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