Build a production-ready LangGraph agent that plans, executes, validates tools, persists state, remembers context, and serves a deterministic JSON /agent.

Learn how to build an MCP server in Python to standardize and reuse AI tools, resources, and prompts across applications. This hands-on guide walks you through server setup, client testing, and GPT-4 chatbot integration for production-ready systems.

Prompting gets you started and in-context learning takes you further, but fine-tuning is how you break through performance limits. This guide explains how and when to use it.

Ship a secure self-hosted LLM on Ubuntu. Size hardware, pick models, run vLLM, serve via FastAPI endpoints, choose adaptation confidently.

A practical LangChain intro that teaches prompts, chains, and parsing through a real working example. Build your first LLM workflow the right way.

Fine-tune LLMs on a single GPU using PEFT and LoRA to save memory, ship MB-sized adapters, evaluate outputs confidently, privately.

Learn the basics of RAG by creating an index, searching it manually, and then automating the workflow with LangChain. A practical guide to grounded, reliable LLM applications.

Install Jupyter AI, configure LLM providers, leverage %ai/%%ai to write Python, debug faster, and accelerate data science notebooks dramatically today.

Get clearer, more accurate outputs from reasoning models using concise, structured prompts, smart examples, and chunked context, without forced chain-of-thought instructions.

Learn how to design prompts that give you consistent JSON output. Build stable LLM features, reduce mistakes, and move your production work forward with confidence.