This skill introduces the concept of Retrieval-Augmented Generation (RAG), where external knowledge sources are queried and integrated into the generation process. It covers retrieval pipelines, indexing strategies, and deployment in HPC environments.

• External: Familiarity with LLMs and vector search concepts
• Internal: None

• Define the RAG architecture and explain how it improves generative model performance.
• Describe the components of a retrieval pipeline, including query formulation, embedding, and indexing.
• Identify vector databases and similarity metrics used in AI retrieval tasks.
• Integrate retrieval results into prompt templates or model input streams.
• Evaluate RAG systems based on latency, accuracy, and grounding quality.

Caution: All text is AI generated