Donkit Blog — notes from the RAG mines

• Why Most “Autonomous” AI Tools Break the Moment They Hit Real Data

  Why autonomous AI tools fail on real data — and what actually works.

• Interventional Evaluation for RAG: Why Testing the Happy Path Is Not Enough

  Why robust RAG starts with breaking the pipeline before reality does.

• Combining RAG and RLM for Precision Across Massive Knowledge Bases

  RAG + RLM: deeper, more precise answers across massive enterprise knowledge bases.

• TOON (Token-Oriented Object Notation): When “Long Context” Becomes Affordable

  TOON cuts JSON tokens ~40%, fitting 1.7× more long context

• The RAG Triangle

  Master the trade-offs between accuracy, latency, and cost in RAG.

• What is RAG?

  LLMs are great at language, but bad at facts. RAG fixes it because RAG = "Search first, then answer."