Video narration
You can build a convincing LLM demo with one clever prompt-and watch it break when reliability, scale, and security matter. That's the gap between shipping a quick feature and shipping a product you can trust. This course traces what happens between your prompt and the model's response, then builds your engineering stack. Work through the OpenAI API, completion endpoints, and function calling; the Claude API, system prompts, tool use, and extended thinking; then explore Gemini and DeepSeek, including Gemini's native multimodality. For real workloads, build RAG with smart chunking, re-ranking, and hybrid retrieval, fine-tune your own dataset, and choose and size a vector database. Then prototype agents with advanced function calling and secure them against prompt injection and jailbreaks. Here's the turn: LLM work is not prompt writing. It's engineering the system around the model. Bring it all into one working build, earn certification, and start shipping reliable LLM products.