RAG and Context Engineering: Designing and Building Production-Grade AI Systems
Design retrieval-aware AI systems for real-world deployment
- START DATE July 29, 2026
- TIME COMMITMENT 8-10 hrs/week
- DURATION 8 Weeks
- FORMAT Online
- PRICE $5,600
The RAG and Context Engineering: Designing and Building Production-Grade AI Systems program from MIT xPRO is an eight-week learning journey that equips technical professionals to design, evaluate, and deploy retrieval-aware large language model (LLM) systems that perform reliably in real-world environments. Organizations today increasingly need experts who can architect and strengthen AI systems against inconsistent outputs, security vulnerabilities, and performance bottlenecks.
In eight weeks, this program builds expertise in evaluation discipline, system auditing, retrieval-augmented generation (RAG) design, and the core principles that determine whether LLM systems succeed or fail in production. Through faculty-led live online sessions, guided labs, applied exercises, and a capstone project that requires you to build and evaluate a production-grade system end to end, the curriculum takes you from understanding the mechanics of LLMs to designing retrieval-augmented systems built for accuracy and safety.
WHAT YOU WILL LEARN
- Decide when and why external retrieval is necessary in LLM systems
- Design classical, semantic, and hybrid retrieval pipelines
- Diagnose accuracy and performance failures using structured evaluation
- Build end‑to‑end RAG systems and advanced multihop retrieval pipelines
- Implement retrieval‑aware agentic workflows for multistep reasoning
- Deploy secure and observable production‑grade RAG systems
Who Should Enroll
Generative AI and LLM system builders, including AI or machine learning (ML) engineers, software engineers, full stack or back-end developers, data scientists, and ML operations engineers
Technical professionals in hybrid or adjacent roles, including technical product managers or program managers and solution architects
Software engineers with practical Python experience
Prerequisites: Participants must have a functional knowledge of Python, application programming interfaces (APIs), and AI/ML concepts.
How You Will Learn
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LEARN BY DOING
Practice processes and methods through simulations, assessments, case studies, and tools.
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LEARN FROM OTHERS
Connect with an international community of professionals while working on projects based on real-world examples.
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LEARN ON DEMAND
Access all of the content online and watch videos on the go.
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REFLECT AND APPLY
Bring your new skills to your organization, through examples from technical work environments and ample prompts for reflection.
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DEMONSTRATE YOUR SUCCESS
Earn a Professional Certificate and CEUs from MIT xPRO.
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LEARN FROM THE BEST
Gain insights from leading MIT faculty and industry experts.
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