Timeline
New RAG paradigm with iterative retrieval at multiple reasoning steps achieves 15-20% accuracy gain on HotpotQA
Positioned as technique for mastering specific style, tone, or domain logic in AI model adaptation
Positioned as go-to technique for dynamic, fact-heavy applications with frequently changing information
Research exposed a critical vulnerability where just 5 poisoned documents can corrupt RAG systems.
Clarification article published explaining distinction between fine-tuning and RAG for LLM applications
Clarification article published explaining distinction between RAG and fine-tuning for LLM applications
Publication of a framework moving RAG systems from proof-of-concept to production, outlining anti-patterns and a five-pillar architecture.
Ethan Mollick declared the end of the 'RAG era' as dominant paradigm for AI agents
Fine-tuning is argued to be losing its potency as a unique differentiator in favor of data-first approaches
Ecosystem
Fine-Tuning
Retrieval-Augmented Generation
Evidence (10 articles)
Enterprises Favor RAG Over Fine-Tuning For Production
Mar 23, 2026A Comparative Guide to LLM Customization Strategies: Prompt Engineering, RAG, and Fine-Tuning
Mar 28, 2026Fine-Tuning vs RAG: Clarifying the Core Distinction in LLM Application Design
Apr 14, 2026Fine-Tuning vs RAG: A Foundational Comparison for AI Strategy
Apr 22, 2026When to Prompt, RAG, or Fine-Tune: A Practical Decision Framework for LLM Customization
Mar 30, 2026Mistral Forge Targets RAG, Sparking Debate on Custom Models vs. Retrieval
Mar 25, 2026RAG vs Fine-Tuning vs Prompt Engineering
Apr 21, 2026RAG vs Fine-Tuning: A Practical Guide to Choosing the Right Approach
Mar 17, 2026+ 2 more articles