What is RAG? A Beginner's Guide to Retrieval-Augmented Generation (For Engineers Who Actually Build It)
Retrieval-Augmented Generation (RAG) is a method that enhances AI models by providing them with relevant context from a knowledge base before answering user queries. This approach addresses the limitations of AI models, which often lack up-to-date information about specific company policies or documentation. RAG is simpler and more efficient than traditional fine-tuning or prompt engineering methods, making it a preferred choice for many applications.
- ▪RAG helps AI models answer questions by providing them with relevant context from a knowledge base.
- ▪It does not retrain models but instead retrieves information to enhance responses.
- ▪RAG is more efficient and cost-effective compared to fine-tuning and prompt engineering.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3939285) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } G V NIKITHA Posted on May 26 What is RAG? A Beginner's Guide to Retrieval-Augmented Generation (For Engineers Who Actually Build It) #ai #beginners #llm #rag RAG sounds complicated. It's not. But a lot of introductions to RAG make it sound more mysterious than it actually is. They use terms like "semantic search" and "vector embeddings" and "retrieval pipeline" before explaining what the actual problem is. So let me start differently.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
