The RAG Autonomy Spectrum: A Guide to Designing Smarter AI Systems

The text explores cognitive architecture patterns for LLM-powered applications, focusing on Retrieval Augmented Generation (RAG) systems. It categorizes these architectures into six levels based on autonomy, from hard-coded steps to fully autonomous agents. Classic code without LLMs is considered level one. Subsequent levels progressively integrate LLMs for tasks like translation, chaining multiple LLM calls, and routing decisions. State machines introduce cycles for adaptive refinement. Autonomous agents can choose tools and refine instructions independently. RAG is presented as a solution to LLM limitations, offering factual grounding and access to real-time data. The text also ranks various RAG techniques based on autonomy levels, illustrating real-world applications. RAG levels range from classic search to chained, router-based, state-machine-driven, and fully autonomous approaches. The article ultimately advises selecting the appropriate level of autonomy based on the specific application's requirements and complexity.