Why RAG is the most practical AI feature you can ship in 2025

Retrieval-Augmented Generation (RAG) is the bridge between frozen foundation LLMs and your proprietary dynamic data. While building fully autonomous agents makes for great social media demos, RAG is the workhorse of enterprise AI. It is practical, audit-able, and cost-effective. ## Why RAG Wins Over Fine-Tuning in 2025 1. **Real-time updates**: Fine-tuning cannot keep up with rapidly changing files or databases. RAG updates as fast as your index does. 2. **Access Control**: You can easily restrict search results based on user permissions, something nearly impossible with fine-tuned models. 3. **Traceability**: RAG outputs cite their sources, giving users a way to double-check AI answers. ## Our Recommended Stack At KrissDevHub, we consistently see success with: - **Next.js** for custom search UI. - **pgvector** inside Supabase for vector storage. - **Cohere / OpenAI** for embeddings and reranking.