Every LLM application eventually runs into the same wall: the model generates plausible-sounding output that is wrong, off-format, or unsafe — and there is nothing between generation and delivery to catch it. Prompting the model harder helps sometimes. However, it is not reliable.

This workshop teaches a systematic approach to the problem using two open-source IBM tools: Mellea, a Python library for structured LLM generation, and Granite Libraries, a collection of lightweight LoRA adapters that score generated output against developer-defined requirements. Together they implement an Instruct-Validate-Repair loop — generate a response, measure it against your requirements, and select or retry before it reaches the user.

Participants start with a plain chatbot that hallucinates citations, ignores formatting rules, and produces uncontrolled output. By the end of the session, the same chatbot validates every response against a set of requirements defined in plain English, generates multiple candidates in parallel, and automatically selects the best one — all running locally, all on open-source models.

No cloud accounts, no audio hardware, no frontend build. A working environment takes under five minutes to set up.

What you will build: a command-line chat application that grows module by module — from a bare Mellea generation call, to single-requirement scoring, to a parallel Best-of-N validation loop with multiple Granite Libraries adapters firing simultaneously.

What you will leave with: a mental model of how to enforce output quality programmatically, hands-on experience writing and tuning natural-language requirements, and a local codebase you can adapt to your own domain.

Technologies covered: Mellea, Granite Libraries (activated LoRA adapters), IBM Granite 4.0, Python, OpenAI-compatible inference backends (LM Studio, Ollama, vLLM).

All tools and models used are Apache 2.0 licensed and available on HuggingFace.