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Generative AI Guides & Tutorials
Practical guides to LLMs, RAG, agents, prompt engineering and the modern Gen AI stack used in production today.
Generative AI articles
Building a Customer Support Bot with LLMs
A useful support bot knows when to answer and when to hand over. This guide sets out the architecture of an LLM support bot, then routes 3,080 real banking queries to 77 intents with a small embedding model and shows how a confidence threshold trades the share the bot answers against how often it is right.
Building a Document Q&A Chatbot with RAG
Answering one question about a document is the easy part. A chatbot also gets follow-ups such as which one, and why. This guide builds a document Q&A chatbot over a 48-page PDF with page citations, runs one conversation two ways, and shows how rewriting follow-ups turned a wrong answer into a right one.
Cost Optimization for LLM Applications in Production
LLM bills grow with every token, and most applications waste many of them. This guide breaks a request's cost into its parts, measures prompt caching and output length on a local model, then prices five options for a month of traffic using published list rates, from no changes to a cached small model.
Deploying a Gen AI App on the Cloud: A Walkthrough
A notebook that calls a model is not an application. This walkthrough turns one into a small web service with input limits, configuration from the environment, friendly failure handling and fast tests, then packages it in a container and deploys it to Google Cloud Run with the model key held as a secret.
Generative AI for Images: Diffusion Models Explained
Diffusion models create images by learning to undo noise. This guide explains the forward noising process, what the network is trained to predict, why sampling takes many steps, and how latent diffusion and text guidance produce today's image generators, with a small NumPy demonstration of the maths.
Generative AI Interview Questions and How to Answer Them
Generative AI interviews have settled into a pattern: fundamentals, retrieval, adaptation, agents, evaluation and a design problem. This guide gives eighteen common questions with concise answers, says what each one is really testing, and explains how to prepare with measured evidence from your own projects.