Neha Singh

Retrieval‑Augmented Generation (RAG) systems promise grounded, trustworthy answers—but only if we can measure  how well they actually perform. Over the past few weeks, I deep‑dived into RAGAS , an evaluation framework purpose‑built for RAG pipelines. In this post, I’ll walk through my hands‑on journey with RAGAS: Creating and saving evaluation datasets from scratch Understanding and applying core metrics like Faithfulness , Answer Relevancy , and Context Precision Running experiments...

Building an autonomous agent that fixes its own SQL queries in real-time. Coming soon...

Large Language Models (LLMs) are incredible, but asking them to do everything in one go, especially for critical tasks like PII (Personally Identifiable Information) sanitization, is often a recipe for disaster. We've all seen prompts like "Extract PII and summarize." While convenient, this approach often sacrifices accuracy for simplicity, leaving sensitive data vulnerable. In this post, we'll dive into how to build a production-ready PII Sanitization Chain  using LangChain Expression...