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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
Model Context Protocol (MCP) Explained for Beginners
MCP is an open standard for connecting AI applications to tools and data. Instead of writing a custom integration for every assistant, you write one server and any compatible application can use it. This guide explains the parts and runs a real server and client, so you can see exactly what passes between them.
Ollama Tutorial: Run LLMs Locally on Your Machine
Ollama runs open language models on your own computer with one install. This tutorial walks through the command line, the REST API, conversation history, the OpenAI-compatible endpoint and a custom model built from a Modelfile, all run on an ordinary laptop, with the settings and limits worth knowing.
Evaluating LLM Outputs: Metrics, Tools and Best Practices
Every change to a prompt or model needs evidence that it helped. This guide explains the main ways to evaluate LLM outputs, then checks a small local LLM judge against expert human verdicts from MT-Bench and finds it agreeing often, flipping often when answers swap places, and trustworthy only when consistent.
Tokenization in LLMs: Why It Matters More Than You Think
Models do not read words. They read tokens, and tokens decide what a prompt costs, how much fits in the context window and why a model stumbles on spelling and long numbers. This guide explains subword tokenization and measures it: one sentence is 8 tokens in English and up to 99 in Telugu.
Prompt Engineering: Techniques That Actually Improve Output
Most prompt advice is a list of tips. This guide takes one job, pulling three fields out of customer emails, and rewrites the prompt seven times, scoring each version on the same twelve emails. An example and a schema fixed the format; definitions and asking for evidence before the verdict fixed the meaning.
Agentic AI Roadmap 2026: From Prompts to Autonomous Systems
Agentic AI is the most hyped corner of generative AI and the easiest to learn in the wrong order. This roadmap builds it in seven stages, from reliable prompts and structured output through retrieval and tools to workflows, agents and production safeguards, with something to build at each stage.