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SHORT READ
RAG vs Fine-Tuning Decision Guide for Production
A practical framework for deciding when retrieval beats fine-tuning and when model adaptation is worth the cost.
AI EngineeringLLMsArchitecture
Go Deeper
This short read is the fast path. The linked long-form post covers full architecture, tradeoffs, and implementation details.
Read the Full RAG Systems GuideAdditional Reads
Trusted references that add context beyond nat.io and help you validate decisions faster.
- RAG for Knowledge-Intensive NLP arXiv
Foundational retrieval-augmented generation framework.
- Fine-tune models with Azure OpenAI Microsoft Learn
Practical guidance for when tuning beats prompt-only methods.
- AI Risk Management Framework (AI RMF) NIST
A practical baseline for governing risk across AI systems.
