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.

Build a production-ready multi-agent chatbot with analyst and reviewer agents, ChromaDB RAG, CrewAI, and Gradio, delivering clearer, verified answers consistently.

Preserve Word layouts, tables, and media with Python GPT-4o hybrid workflow; combine text extraction and page images for accurate transcription.

Build a private DeepSeek-R1 chatbot with Ollama, MongoDB, and chat U. No external APIs. Deployment steps for local setups or AWS.

Ship a Python knowledge graph chatbot using Neo4j, Chainlit, and GPT-4o: Auto-generate Cypher, visualize results, and answer complex data questions accurately.

Understand LLMs fast so you can take your first step into practical AI, learning what they are, how they're built, where to find them, and how to use them effectively.