AI learning path
Understand transformer-based language systems, build retrieval with citations, and evaluate quality before treating a demo as a product.
For AI application developers and technical product teams
6 resources
Full Stack Deep Learning
A practitioner-focused course covering prompt engineering, augmented language models, LLMOps, deployment, user experience, and product development.
Hugging Face
A practical notebook for creating a synthetic question set and evaluating retrieval-augmented answers with automated judges and explicit metrics.
LlamaIndex
An official starter path through document loading, indexing, retrieval, querying, and inspection of a compact RAG application.
LangChain
A guided implementation of retrieval and agentic RAG using loaders, embeddings, vector stores, tools, and a model-driven workflow.
OpenAI Developer Documentation
Guidance for defining objectives, collecting representative cases, selecting graders, and continuously evaluating model behavior.
Stanford NLP
An end-to-end technical course on data pipelines, tokenization, transformer implementation, training, scaling, evaluation, and systems efficiency for language models.
