rags

6 stories

Stories

Topic: rags

The Evolution of Fine-Tuning: From Retraining Everything to Rewarding Correctness
Last month I had a model, a dataset, and a GPU already warm. The old reflex said fine-tune. I didn’t. I sat there and asked whether…Continue reading on Towards AI »
Towards AIOct 10, 20261 min read
The Chunking Dilemma: Why RAG Fails Before Retrieval Even Starts
Why 80% of enterprise RAG failures happen before retrieval starts. Learn the Goldilocks chunking dilemma, 512-token truncation, and production splitting rules.
Towards AIOct 7, 202615 min read
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG
From Retrieval to Governance — The Architecture Shift Replacing Naive RAG Stop asking “How do we retrieve more chunks?” Start asking “How do we govern and structure what the model …
Towards AIOct 5, 202612 min read
Vector Search Is Not Enough: The Blueprint of GraphRAG
Vector Search Is Not Enough: The Blueprint of GraphRAG Vector search tells you what is similar; GraphRAG tells you how it is connected. Retrieval-Augmented Generation (RAG) has emerged as an …
Towards AIOct 5, 202611 min read
Building Knowledge Vault : From RAG Implementation to Retrieval Engineering
I built a local-first PDF RAG system, then measured retrieval, reranking, latency, evidence quality, and failure modes to understand where it breaks.
Towards AIOct 1, 202616 min read
What is RAFT? RAG + Fine-Tuning
What is RAFT? RAG + Fine-Tuning In simple terms, retrieval-augmented fine-tuning, or RAFT, is an advanced AI technique in which retrieval-augmented generation is joined with fine-tuning to enhance …
Becoming Human (AI)Aug 12, 20264 min read