Rag - TechnoScripts

rag

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

RAG System Design: The LLM Interview Whiteboard Round

RAG system design, short for Retrieval-Augmented Generation, is the GenAI whiteboard round where an interviewer says “design ChatPDF for an enterprise” and then watches how you think for forty-five minutes, not what you can draw. This post walks that round end to end: an eight-layer framework, two fully worked designs, the follow-up gauntlet on ACLs, freshness, and scale, and a mock transcript with the rubric, every number computed with real code. “They grade the numbers you defend, not the boxes ... Read More

Choosing an AI Tech Stack: 3 Real Case Studies

Teams pick the shiny tool first, bend the problem to fit it, and six months later a large language model is doing a job a hundred lines of scikit-learn would have nailed. Choosing an AI tech stack is where projects quietly go wrong before any model code exists. This post gives you a four-axis decision framework, then walks three real case studies to very different, defensible stacks. “The best stack is the boring one that clears your acceptance metric and ... Read More

ML System Design: Recommenders, Feature Stores, Skew

Most interview rounds ask you to write a function; the ML system design round asks you to design a whole machine learning product out loud, and it is the stage that most often decides a senior offer. Given a vague goal like “recommend videos” or “catch fraud”, can you turn it into metrics, data, features, a model, a serving path, and monitoring? This post gives you a fixed skeleton, two fully worked designs, and a graded mock transcript. “Anyone can ... Read More