Rag - TechnoScripts

rag

Guides to retrieval-augmented generation, combining search with language models for grounded answers.

LLM and GenAI Interview Questions: 40-Question Checkpoint

GenAI screens do not chase whichever model launched last week; they recycle a fixed set of durable ideas in slightly different words. The LLM interview questions in this checkpoint cover that set, forty in all, grouped the way real screens group them, each with a short spoken-style answer, runnable code for the numbers people fumble, and a link to the deep-dive post that teaches it in full. Then you score yourself, get a revisit list built from exactly what you ... Read More

RAG Project: Build, Evaluate, and Ship a Retrieval App

Anyone can wire a Large Language Model (LLM) to a folder of documents and get answers back. Proving those answers are good is a different job, and it is the one interviewers actually probe. This RAG project is the capstone for the GenAI chapter: build a document-question app, score it with real numbers, trace every request, and gate every merge behind CI. “If you cannot measure it, you cannot improve it, and you certainly cannot ship it.” Last Updated: July ... Read More

LLM-as-a-Judge: Online Evals, CI Gates, and Canary Sets

Your chatbot ships, users complain, and the dashboards show nothing wrong. Free-form text has no single right answer to diff against, which is why teams use an LLM as a judge to put a number on quality. The LLM evaluation tutorial scored builds offline, before anyone shipped; this post drags that judge into production with agreement audits, canary sets, and CI gates. The uncomfortable truth of shipping language models is that evaluation is the new system design. You do not ... Read More