In the news
Fine-Tuning vs RAG vs Prompt Engineering: How to Adapt an Open Weight Model
Fastino · Published · 3 min read
In 30 seconds
- What happened
- Guide comparing five techniques to adapt open weight models: prompt engineering, RAG, supervised fine-tuning, preference optimization, and reinforcement learning.
- Why it matters
- Engineers deploying base models to production need to choose which adaptation strategy fits their accuracy, cost, and freshness requirements.
- Watch out
- No single technique solves all problems. RAG fails with poor retrieval, fine-tuning locks in stale facts, and RL requires reliable reward signals or it teaches wrong behavior.
- rag
- prompt
- fine-tun
The patterns behind this
- Reinforcement Learning from Human Feedback
- Agentic Context Engineering (Evolving Playbook)
- Automatic Prompt Optimization
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.