LLM-engineer-handbook

Why we included this project

Most engineers can put together an LLM demo in an afternoon, but closing the performance, security, and scalability gaps that turn a prototype into something dependable is the hard part. This handbook gathers the frameworks, tutorials, datasets, and benchmarks that span the whole LLM lifecycle, from pretraining and fine-tuning through serving and LLMOps, so you can jump straight to the stage you're working on. It also keeps a place for classical ML, which still shows up in production for things like hallucination detection and data privacy. Whether you're assembling a working stack or planning what to learn next, this is a practical map to the ecosystem, not a tool you deploy.