🛠️I spent 10 hours last week mapping out a RAG pipeline. Three things became clear:Week 1 of building this from scratch was intense but rewarding. I wanted to understand how the pieces fit together before jumping into implementation. That investment paid off.The pipeline itself brings together three main components: a knowledge base search index, a prompt that includes user input plus retrieved context, and the LLM model itself. Pretty neat stuff once you see how they connect.What stood out most was the value of modular design. You can swap models, clients, or data indexes down the road without rewriting everything; huge win for long-term flexibility. I especially like this approach because it keeps things adaptable as needs change.As an extra challenge, I’m experimenting with Local AI instead of cloud APIs. The appeal is privacy, which has always mattered to me. So far it’s as simple as switching the API response call. No major overhaul needed; just a tweak in where responses come fro...
🛠️I spent 10 hours last week mapping out a RAG pipeline. Three things became clear:Week 1 of building this from scratch ...
