9
LLM Engineering
Vector databases, RAG pipelines, LangChain, and building AI agents
5 lessons250 min totaladvanced
1
Vector Databases & Semantic Search
Embeddings, vector search concepts, ANN algorithms (HNSW, IVF), Pinecone/Chroma/Weaviate/pgvector comparison, metadata filtering
2 exercisesQuiz45m
2
Production RAG Pipelines
End-to-end RAG, document loading, chunking strategies, retrieval strategies (dense/sparse/hybrid/reranking), query transformation, RAGAS evaluation
2 exercisesQuiz55m
3
LangChain & LlamaIndex
LangChain core (chains, prompts, memory, output parsers, LCEL), LlamaIndex (nodes, indices, query engines), comparison, common patterns
2 exercisesQuiz50m
4
Building AI Agents
ReAct pattern, tool use/function calling, planning, multi-agent systems, frameworks (LangGraph, CrewAI), guardrails
2 exercisesQuiz55m
5
LLM Evaluation
Automated metrics (perplexity, BLEU, ROUGE), LLM-as-judge, benchmarks (MMLU, HumanEval, HELM), A/B testing, eval frameworks (LangSmith, Ragas)
2 exercisesQuiz45m