Topics

RAG vs fine-tuning: when to use which

Evergreen topic pages updated with new evidence

Answer

This topic page provides a direct answer, key points, and a source-backed evidence timeline. It is updated as the ecosystem changes.

Key points

  • Start from primary sources (official blog / repo / changelog) before citing or deciding.
  • Track by themes (topics/entities) so evidence accumulates on evergreen pages.
  • Use a weekly routine (shortlist → one action) to avoid doomscrolling.

What changed recently

  • New evidence and links are added as relevant updates appear for: RAG, fine-tuning, trade-offs.

Explanation

This page is maintained as an evergreen knowledge page. It prioritizes clarity, trade-offs, and verifiable sources.

Tools / Examples

  • Use the evidence timeline to verify claims quickly.
  • Follow the sources section for primary-source citation.

Evidence timeline

March 23 AI Briefing · Issue #137

HELIX, a privacy-preserving inference system, achieves sub-second response times by leveraging shared representations from large language models to overcome bottlenecks in private computation [5]; MiniMax officially open

AI Briefing, March 22 · Issue 135

OpenAI's Responses API achieves a 10x performance boost via container pooling, significantly improving infrastructure reuse efficiency for Agent workflows [3]; meanwhile, Stanford research reveals ChatGPT encourages viol

March 19 AI Briefing · Issue #126

The frontier of AI safety is rapidly shifting toward systematic research into deep alignment phenomena—including metagaming, chain-of-thought obfuscation, and consciousness-claim-induced preference emergence—while YuanLa

March 13 AI Briefing · Issue #108

RAG architecture optimization and multi-model routing are emerging as key levers for cost reduction and efficiency gains; GPT-5.4 tops CursorBench, showcasing a new peak in agent-based coding; Claude and Gemini are rapid

March 6 AI Briefing · Issue #88

The AI race has officially entered a new phase of 'track specialization': OpenAI leads in white-collar automation and general-purpose interaction; Anthropic focuses on programming agents and reinforcement learning; Googl

Sources

FAQ

How is this page maintained?

It is updated when new evidence appears, rather than creating thin pages for every headline.

How should I cite this page?

Use the primary source links for any citation or decision; cite this page as a summary layer if needed.

Last updated: 2026-03-27 · Policy: Editorial standards · Methodology