AI Engineer Portal
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RAG Systems
Build retrieval systems that are explainable, measurable, and debuggable.
Document ingestion and chunking
Choose chunking strategies, extract metadata, and handle different file formats so retrieval has clean, meaningful inputs.
Document ingestion and chunking
Choose chunking strategies, extract metadata, and handle different file formats so retrieval has clean, meaningful inputs.
Embedding and vector storage
Choose embedding models and vector databases, understand similarity metrics, and build an index you can actually query.
Retrieval patterns
Implement semantic search, hybrid BM25 plus vector retrieval, reranking, and diversity strategies to maximize context quality.
RAG evaluation and debugging
Measure faithfulness, context relevance, and answer quality separately, then diagnose why a RAG pipeline is giving wrong answers.
Production RAG
Handle caching, latency optimization, incremental indexing, cost at scale, and monitoring in a RAG system that real users depend on.
Advanced RAG patterns
Go beyond naive RAG with multi-hop reasoning, self-checking retrieval loops, and corrective fallback strategies for hard questions.
RAG with LangChain
Build production RAG pipelines using LangChain document loaders, text splitters, retrievers, and chains — and understand when to use LangChain versus a custom implementation.
RAG in production: scaling and monitoring
Operate a RAG system at scale with index lifecycle management, multi-layer caching, retrieval quality monitoring, and cost optimization strategies.