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Almog Baku

Almog Baku

LLM engineering expert and startup entrepreneur bringing AI innovation to life

AlmogBaku
Israel
Tel Aviv, Israel
Joined December 2025
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Summary

Almog Baku is a prolific entrepreneur and founder, having co-founded a stealth startup, Raptor.ml, Rimoto, and Natun.ai. His entrepreneurial journey demonstrates a consistent drive to build and innovate within the tech and AI sectors. almogbaku+2
Almog is a leading expert in LLM engineering, with a strong focus on building and deploying production-ready AI applications. He is the author of 'The LLM Triangle Principle,' a framework for reliable LLM apps, and has consulted extensively on LLM development, deploying dozens of applications. almogbaku+3
Almog is deeply involved in the open-source community, notably as a Kubernetes maintainer since 2016. He has also developed several open-source tools for LLM development, such as 'openai-streaming,' 'pytest-evals,' and 'LLM Playground,' showcasing his commitment to sharing knowledge and tools. almogbaku+2
He is a prominent community builder, having founded GenAI Israel, the largest Generative AI community for practitioners in Israel with over 7,000 members. He also hosts the 'AI Infra Stories' podcast and is a frequent speaker at conferences and meetups on AI and LLM topics. almogbaku+3

Work

Projects

Writing

8 Practical Prompt Engineering Tips for Better LLM Apps

August 1, 2024

Essential tips for effective prompt engineering in LLM applications, based on direct implementation experience, aimed at improving performance, production readiness, and optimized workflows.

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The LLM Triangle Principles: Software Design Principles for Reliable LLM Apps

July 1, 2024

An innovative approach to designing robust LLM-based applications for real-world use, derived from hands-on project experience. It presents a framework to bridge the gap between potential and production-grade performance.

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Building LLM Apps: A Step-By-Step Guide

June 1, 2024

A comprehensive guide to LLM application development, covering everything from experimentation to production, based on personal implementation experience.

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Effective AI Infrastructure Explained

April 1, 2021

Explores modern AI infrastructure and its impact on the ML lifecycle, informed by hands-on project work and cloud-native expertise.

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