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Live LLM Cases
Six end-to-end walkthroughs: production-ready RAG chatbot, tool-using agent, model comparison and evaluation, token cost optimization for high-volume apps, hallucination guardrails, and fine-tuning a small model for domain specialization.
Module Content
Case 1 — Build a Production-Ready RAG Chatbot
End-to-end RAG chatbot over your documents. Hybrid search, reranking, citations, fallback.
Case 2 — Add Tool Use to an LLM Agent
Build an agent that uses tools to answer customer questions about orders, shipping, and refunds.
Case 3 — Evaluate Two LLMs for a Specific Task
Compare Claude Sonnet vs GPT-4o-mini vs Llama 70B on a real classification task. Build the eval, run it, decide.
Case 4 — Optimize Token Costs for a High-Volume App
Your RAG app costs $30K/month. Halve it while maintaining quality.
Case 5 — Diagnose Hallucinations and Add Guardrails
Your LLM app is making things up. Track down where it happens and stop it.
Case 6 — Fine-tune a Small Model for Domain Specialization
Distill Claude Opus into a fine-tuned Llama 8B for production cost savings.