AI 401: Under the Hood of RAG Architecture
Comprehend RAG architecture: how document retrieval, embedding, and context injection work together
Retrieval-Augmented Generation (RAG) is the technology that allows an AI to answer questions using a custom knowledge base. Understanding its three-stage architecture—Embedding, Retrieval, and Augmentation—is key to diagnosing why it sometimes fails and to building more effective document-based workflows.
Related topics
- Grasp context window constraints: memory limits, conversation coherence, and strategies for long documents
- Predict when retrieval will fail: document too large, poor chunking, semantic mismatch between query and content
- Understand attention mechanisms: how transformers weigh importance of different input parts when generating output
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