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DESCRIPTION:This course teaches working software engineers how modern AI systems are built, from LLM internals and vector search to production‑ready RAG and agentic applications.
𝗪𝗛𝗔𝗧 𝗬𝗢𝗨 𝗪𝗜𝗟𝗟 𝗟𝗘𝗔𝗥𝗡:
➽ Grasp core LLM basics: tokenization, attention, transformers, and how LLMs generate text.
➽ Use effective prompt engineering: structured prompts, zero‑shot/few‑shot, and simple prompt chains.
➽ Build RAG systems: create chunks, generate embeddings, and retrieve the right context for answers.
➽ Use vector databases: store embeddings and tune similarity search and indexing for speed vs. accuracy.
➽ Improve model serving: apply KV caching, quantization, and batching to balance latency and cost.
➽ Embed AI into products: connect LLMs with your app’s APIs, tools, and business logic.
➽ Understand agents and MCP: let agents call tools and link external systems to power complex AI workflows.
❉ L I N K: ❉