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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.

Introduction Most AI assistants are good at answering questions but poor at remembering what you asked them to do yesterday. They have no persistent state across conversations — every session starts fresh. The Model Context Protocol (MCP) solves this by letting you build external tools that Claude (or any MCP-compatible host) can call during a conversation, with results persisted wherever you choose. In this tutorial, we build an MCP To-Do List Manager — a local server that g

Semantic chunking is a chunking strategy that groups text based on meaning rather than fixed size. Instead of splitting text after a certain number of tokens, semantic chunking tries to identify: topic boundaries, semantic transitions, and coherent conceptual units. The goal is simple: Keep semantically related information together.

Sliding window chunking has become one of the most widely used retrieval strategies because it helps preserve continuity between chunks without requiring complex semantic analysis.

Introduction Most AI email tools work as a single prompt: paste your draft, get a rewrite. The problem is that rewriting well requires two very different cognitive tasks — understanding what is wrong with the email, and then knowing how to fix it. Combining both into one prompt produces mediocre results for the same reason that asking a single person to be both a critic and a writer at the same time produces weak output. In this tutorial, we build an A2A Multi-Agent Email Dra

Chunking is the process of splitting documents into smaller retrievable units before embedding and indexing them. In a RAG pipeline: Documents are split into chunks. Each chunk is converted into embeddings. The embeddings are stored in a vector database. User queries retrieve the most relevant chunks. The retrieved chunks are passed to the LLM as context. This means retrieval quality depends heavily on chunk quality.

Introduction Not every piece of text needs the most powerful language model to check it. A short sentence with a grammar error can be caught by a fast, cheap model in under a second. Only long, complex writing with structural and coherence problems genuinely benefits from the most capable model available. Tiered model routing applies this logic systematically. Short to medium text (up to 100 words) goes to GPT-4o-mini for grammar and clarity. If it detects structural or coher

Introduction Finding the next great book is harder than it sounds. Generic bestseller lists ignore your taste, and search engines return the same ten titles for every query. What most readers need is a recommendation that actually understands them — their preferred themes, emotional tone, narrative pace, and the books they already love. In this tutorial, we build an AI-powered Book Recommender using Kimi K2, Moonshot AI’s flagship agentic model. The user describes their readi