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bimal1023/AI-native-engineer

Tracking my journey into AI engineering, one module at a time. Fundamentals to agents, with hands-on projects. Open to all.

8 stars
1 forks
momentum ▲ 18.0
created 2026-08-08
on radar since 2026-08-10
star trend 3 → 8 since 2026-08-10
aiai-agentai-agentsai-engineeringai-modelai-toolsaiengineerautomationlearning-resourcesllmllm-evaluationllmopsmcp-servermlopsragtokenization
View on GitHub ↗

About AI-native-engineer

A structured, opinionated curriculum for software engineers moving into AI/ML engineering — the discipline that separates someone who can call an LLM API from someone who can design, evaluate, ship, and operate AI systems that hold up in production. It's built as plain markdown: seven modules from foundations to frontier, each with canonical resources, the tools actually used in industry, a hands-on project, and the pitfalls that bite people first. I'm writing it as I learn (publicly, mistakes included), and it's open for anyone making the same transition — self-taught devs, backend/full-stack engineers adding AI to their scope, and new grads who want depth beyond "I used the OpenAI SDK once."

Last reviewed: August 2026 · See hot-topics.md for what's moving fast vs. what's settled.

Module What you'll be able to do -------------------------------------- 01 LLM Fundamentals Explain what the model is actually doing, and pick one on evidence 02 Prompting & Context Engineering Get reliable, structured output and manage the context window as a budget 03 Retrieval & RAG Build retrieval that measurably finds the right thing 04 Agents & Tool Use Design agent loops that terminate, recover, and stay in budget 05 Evaluation & Observability Replace vibes with numbers, offline and in production 06 Deployment & AI Infra Serve models at a known cost, latency, and failure profile 07 Emerging Topics Evaluate new capabilities without chasing demos

From the project README.

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