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