The specific path toward building real LLM-powered applications.
6 min read · Skill IT Education
Before RAG or agents, get comfortable calling LLM APIs directly and understanding parameters like temperature and context window.
RAG is the technique that makes LLMs actually useful for your own data — chunking, embedding, and retrieval are core, non-optional skills.
Once RAG is solid, agent design and production concerns — latency, cost, evaluation — become the differentiators between a demo and something people actually rely on.
This article is the short version. The full program covers it hands-on, in labs, with a mentor reviewing your work.
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