Context engineering is the most in-demand skill for building reliable AI applications in 2026 and beyond. If you’ve ever struggled with LLMs hallucinating, ignoring instructions, or losing track of information in long conversations, the problem usually isn’t the model — it’s the context you’re feeding it. This course teaches you how to systematically design, assemble, and optimize context for large language models, RAG systems, and AI agents so your applications perform consistently in production.
You’ll start with the fundamentals of context windows, token economics, and attention dilution, then move into practical, hands-on skills: building production-grade Retrieval-Augmented Generation (RAG) pipelines with chunking, hybrid search, and reranking; designing short-term and long-term memory systems for chatbots and agents; engineering context for multi-step AI agents and tool use; and evaluating context quality with real metrics and observability tools.
By the end of this course, you will be able to:
- Design context assembly pipelines that reduce hallucinations and improve LLM accuracy
- Build and evaluate production-ready RAG systems using modern retrieval and reranking techniques
- Implement memory architectures for conversational AI and autonomous agents
- Apply enterprise-grade patterns for scalable, secure, and cost-efficient LLM applications
- Debug and audit context failures using observability and evaluation frameworks
