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Introduction Retrieval-Augmented Generation (RAG) pipelines are widely used to build question-answering systems grounded in private or domain-specific documents. But evaluating whether a RAG pipeline is actually working well is harder than building it. Traditional metrics like BLEU and ROUGE measure surface-level word overlap and miss the semantic quality of answers. Human review is accurate but expensive and slow at any meaningful scale. LLM-as-a-Judge sits between these two

Introduction NVIDIA Nemotron-3 is a family of open models built for reasoning, coding, chat, and agentic workflows. The Nano variant packs strong language understanding into a 4-billion-parameter model that can be fine-tuned on a single 24GB GPU, making it practical for teams who want to adapt a capable base model to their own domain without renting a large training cluster. In this tutorial, we fine-tune Nemotron-3-Nano-4B on a customer support dataset. After training, the m

Introduction Most AI tutorials show you a text box. You type, the model replies, and the whole exchange stays on screen. That covers the mechanics of calling an LLM, but it leaves out what makes voice AI feel genuinely different: the question comes from a microphone, the answer comes back as speech, and the whole thing happens without touching a keyboard. This tutorial builds a working voice AI agent from scratch in Python. Press Enter to start recording, speak your question,

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Every AI team building a product in 2026 eventually hits the same wall. The model works. The demo is good. Investors are happy. And then someone asks the question that changes everything: "How do we know it's actually getting better?" Or worse — six months later: "Why did it get worse after the last fine-tune?" This is the moment a team discovers that building an LLM product and doing LLM research engineering are two different disciplines, staffed by two different kinds of pe

Here's a question worth sitting with: when did you last fine-tune, retrain, or change the prompt on your production model — and how do you know it didn't make things worse? Not "did it feel better in the five examples you tried." How do you know. If your answer is "the demo looked good" or "the team felt like it was more helpful," you're not alone — and you're also flying blind. The Pattern We See Constantly A team ships v1. It works well enough. A few months in, they fine-tu

Most readers skip your articles — not because the content is bad, but because reading takes time they don't have. This post breaks down how AI-powered blog-to-audio platforms work, from architecture to cost to rollout, and how a single article can become audio, a podcast episode, and multilingual content automatically. Includes a free downloadable PRD.

Introduction Before you build agents that use tools, remember conversations, or talk to other agents, it helps to start with the simplest possible thing an LLM app can do: take some text in, send it to a model with clear instructions, and return a useful result. In this tutorial, we build a Text Summarizer and Explainer, a terminal application that takes any block of text and processes it in one of three ways: a short summary, a plain language explanation, or a bulleted list

Introduction Most AI chatbot demos are stateless: every message you send is treated as the first. The model has no idea what you said three turns ago, cannot refer back to details you shared earlier, and cannot build a coherent conversation over time. This is the biggest gap between a demo and a real chatbot. In this tutorial, we fix that. We build an AI Chatbot with Memory that maintains the full conversation history across every turn, passes it to the model on each request,

In this guide, you’ll create a fully functional local RAG pipeline in Python that can: Read custom documents Convert them into embeddings Store them in a vector database Retrieve relevant context Generate grounded answers using an LLM By the end, you’ll have a complete command-line RAG application running locally on your machine.

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You shipped the demo. It looked great. The retrieval worked. The model responded fluently. The investors nodded. The Slack channel celebrated. Then you deployed to production. Within 30 days, your support queue filled with complaints. The model was confidently wrong. It hallucinated facts that were nowhere in your documents. It ignored your output format half the time. It worked fine on the test queries and broke on real user inputs. Your inference bill was 3x the estimate. A

If you are building an LLM-powered product in 2026, writing code or integrating an API is the easy part. The hard part is everything that comes after: How do you know your model actually works on your domain? How do you prove it improved after fine-tuning? How do you stop it from hallucinating in production? How do you align its behavior to what your users expect? These are not product questions. They are LLM training research questions — and most engineering teams do not hav

You followed the tutorial. You copied the code. Your AI chatbot answers questions perfectly on your laptop. Then you try to ship it. The API times out under real load. The vector search returns garbage when the query doesn't match training examples exactly. There is no error handling, so one bad request crashes the whole service. You have no idea if it is even working correctly because there is no logging. The chunking strategy that worked on your sample PDF breaks on a scann

Introduction Understanding how people feel about a product, a service, or an idea is one of the most valuable things a business can do, and it is also one of the tasks where AI consistently outperforms rule-based approaches. A single review can carry joy, frustration, and sarcasm all at once. A rules-based keyword matcher misses this nuance. An LLM does not. In this tutorial, we build a Sentiment Analysis Agent. It is a terminal application that takes any text input, sends it

A Voice AI receptionist is an AI-powered system that answers phone calls — and increasingly, in-app and website voice interactions — on behalf of a clinic, and carries out real conversations with patients in natural, spoken language. It's not an IVR menu ("Press 1 for billing, press 2 for appointments"). It's a system that listens, understands intent, responds conversationally, and — most importantly — takes action on the patient's behalf.