Learn how to design, implement, and optimize a Retrieval-Augmented Generation (RAG) system — from architecture to deployment, with real-world insights and practical code examples.
A deep dive into preventing hallucinations in AI systems — from retrieval augmentation to evaluation pipelines, with practical strategies, code examples, and real-world insights.
A deep dive into optimizing Retrieval-Augmented Generation (RAG) systems—covering indexing, embeddings, caching, vector databases, latency trade-offs, and production readiness.
A deep dive into diagnosing and fixing Retrieval-Augmented Generation (RAG) failures — from poor indexing to hallucination — with practical debugging, testing, and monitoring strategies.
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