According to @DeepLearningAI, its Retrieval Augmented Generation course explains how LLMs generate tokens, why hallucinations occur, and how retrieval-based grounding improves factuality using Together AI’s tooling. According to @DeepLearningAI, the curriculum explicitly explores deployment tradeoffs including prompt length, compute costs, and context limits. According to @DeepLearningAI, this focus on cost and context constraints targets the practical variables practitioners balance when scaling LLM applications.

According to @DeepLearningAI, Retrieval Augmented Generation (RAG) systems enhance information retrieval by combining keyword and semantic search methods, as well as metadata filtering, to locate the most relevant documents. The hybrid search approach improves the precision and accuracy of AI-driven systems, which is critical for developing robust crypto trading algorithms and analysis tools that depend on reliable and contextually relevant data (source: @DeepLearningAI).