Rag
6 articles about rag.
Why Desktop AI Agents Skip RAG and Use Structured Markdown for Memory
Most agent memory systems default to embed-and-retrieve. Desktop agents get better results with structured markdown files loaded by category - faster
Built 4 Knowledge Bases and 3 Rotted - Why Flat Markdown Beats RAG
Flat markdown files with pointers beat comprehensive RAG knowledge bases. After building 4 knowledge bases and watching 3 rot, here is what actually works
Why Belief Extraction Beats Flat RAG for AI Agent Memory
Layered memory architectures with belief extraction outperform simple RAG retrieval for AI agents handling hundreds of conversations. Structured compression
Is RAG Dead? Bigger Context Windows Shift the Use Cases
With context windows growing past 1 million tokens, many RAG use cases are better served by stuffing documents directly into context. RAG is not dead but
Why Standard RAG Is Terrible for AI Agent Long-Term Memory
Retrieval-augmented generation falls apart for persistent agent memory. Knowledge graphs via MCP offer a better path for AI agents that need to remember
Tiered Memory for Desktop Agents - Plain Text First, Vector Search for Long-Term
How desktop AI agents should handle memory: plain text for recent context and vector embeddings only for long-term recall. A practical approach to agent