by harsh-aranga · Agent Tool · ★ 66
AI Engineering Bootcamp A structured, self-paced curriculum to go from "I can call OpenAI API" to building production-grade RAG systems and multi-agent architectures. What This Is This is not a tutorial collection. It's a 10-week learning path designed to build production-ready AI engineering skills. You'll learn: RAG: Chunking, vector stores, hybrid search, reranking, evaluation, debugging bad retrieval Agents: Function calling, LangGraph, state management, memory, human-in-the-loop, multi-agent patterns Production: Observability, LLMOps, hallucination detection, error handling, deployment...
| Stars | 66 |
| Forks | 9 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 64.9823276546491/100 |
| Last Updated | 2026-06-19 |
| Created | 2026-03-20 |
| Platforms | python |
| Est. Tokens | ~14k |
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ai-engineering-bootcamp is A self-paced bootcamp for engineers building AI into production systems. Starts from fundamentals (tokenization, embeddings, prompting) and builds to RAG, Agents (LangGraph, tool design, memory, multi. It is categorized as a Agent Tool with 66 GitHub stars.
ai-engineering-bootcamp is primarily written in Python.
You can find installation instructions and usage details in the ai-engineering-bootcamp GitHub repository at github.com/harsh-aranga/ai-engineering-bootcamp. The project has 66 stars and 9 forks, indicating an active community.
ai-engineering-bootcamp is released under the MIT license, making it free to use and modify according to the license terms.