RAG and Context Engineering: Designing and Building Production-Grade AI Systems
Design retrieval-aware AI systems for real-world deployment
- Learn from MIT faculty in live online sessions
- Discover advanced retrieval, evaluation, and RAG design
- Design RAG systems with guided labs and a capstone project
Program Overview: RAG and Context Engineering
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.
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Key Takeaways: RAG and Context Engineering
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 Is the RAG and Context Engineering Program For?
This MIT xPRO program is designed for technically proficient professionals who are building or contributing to production-grade LLM systems and seek rigorous, engineering-first methods to design retrieval-aware systems that perform reliably at scale. It is ideal for:
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.
Program Highlights
Live Sessions and Virtual Labs - Schedule
Session | Date and Time | |
|---|---|---|
Module 1: Foundations of LLMs for Retrieval-Aware Systems | August 05, 2026 | 1:00 PM - 2:30 PM ET |
Module 2: Retrieval Foundations: From Basic Lookup to Vector Search | August 12, 2026 | 1:00 PM - 2:30 PM ET |
Module 3: Measuring What Matters: Evaluation before Optimization | August 19, 2026 | 1:00 PM - 2:30 PM ET |
Module 4: Advanced Retrieval: MultiHop and Graph-Based Systems | August 26, 2026 | 1:00 PM - 2:30 PM ET |
Module 5: RAG Architectures and Agentic Workflows | September 02, 2026 | 1:00 PM - 2:30 PM ET |
Module 6: Engineering Robust Retrieval-Augmented Systems | September 09, 2026 | 1:00 PM - 2:30 PM ET |
Module 7: Designing for Production: Cost, Security, and Reliability | September 16, 2026 | 1:00 PM - 2:30 PM ET |
What You Will Learn
With faculty led live sessions across eight weeks, the curriculum helps you advance from working with LLMs to building retrieval‑aware systems that integrate information retrieval, enabling accurate, context-aware outputs grounded in external knowledge. The program concludes with a capstone project based on designing your own retrieval-aware LLM system.
Module 1: Foundations of LLMs for Retrieval-Aware Systems
Module 2: Retrieval Foundations: From Basic Lookup to Vector Search
Module 3: Measuring What Matters: Evaluation before Optimization
Module 4: Advanced Retrieval: Multihop and Graph-Based Systems
Module 5: RAG Architectures and Agentic Workflows
Module 6: Engineering Robust Retrieval-Augmented Systems
Module 7: Designing for Production: Cost, Security, and Reliability
Module 8: Capstone — Building and Diagnosing a Production-Grade RAG System
Capstone Project: Build a RAG LLM System
The program includes a capstone project in which you design, implement, and present a retrieval-augmented LLM system from the ground up. Beginning with either of two predefined scenarios, you will design and implement a retrieval‑augmented LLM system incrementally across modules, applying each concept directly to your system. You will progress through building and diagnosing system behavior, evaluating trade-offs, and justifying design decisions related to accuracy, cost, reliability, and security.
At the end of the program, you will present your finished RAG AI system, along with the reasoning behind your design choices and fine-tuning informed by peer feedback. You will also possess:
A working retrieval‑augmented system
A clear understanding of why it succeeds or fails
The ability to explain system behavior to technical and nontechnical stakeholders
Meet the Faculty
Distinguished Professor of Computing; MIT Schwarzman College of Computing; Associate Director and Chief Operating Officer, MIT CSAIL
Armando Solar-Lezama has been a faculty member at MIT since 2008 and has served as an associate director of the MIT CSAIL since 2020. He leads the Computer-Aided Programming G...
Applied Learning: Real-World System Design
Guided lab sessions
Work through complex system-building challenges for one of two provided scenarios, with real-time guidance in virtual lab sessions. Stress-test your understanding of key concepts, ask questions in context, and consolidate your learning before applying it independently to the capstone project.
LLM interfaces and model ecosystems
Experiment with leading LLM interfaces, including ChatGPT, Claude, and Gemini, to compare outputs, test behaviors, and understand model limitations. Use unified access layers, including OpenRouter, to explore how different models perform across tasks.
API-based model access
Gain experience working with LLM APIs, such as OpenAI API, to build structured workflows involving retrieval, chaining, and system design. Learn to generate and manage API keys as part of the implementation
Python-based development
Work within Python environments to implement, test, and iterate on system components. Extend your workflows using development tools, including VS Code and GitHub, to support reproducibility and version control.
Why MIT xPRO?
Founded in 1861, the Massachusetts Institute of Technology (MIT) is recognized globally as a leader in technology, AI, and innovation, driving industry transformation through groundbreaking research and real-world application. Ranked #1 in Forbes America’s Top Colleges list, MIT has built a legacy of excellence in academic rigor, pioneering discoveries, and cross-disciplinary collaboration. MIT xPRO brings this expertise to professionals worldwide, offering executive-level programs that translate cutting-edge research into practical frameworks for leadership, innovation, and impact in a technology-driven world.
Certificate
Upon successful completion of this program, MIT xPRO grants you a digital certificate of completion and 8 Continuing Education Units (CEUs), a globally recognized measure of professional learning that reflects compliance with international quality standards.
This program is graded as complete or incomplete; participants must receive 80% or higher to pass and obtain the certificate of completion.
Notes:
After the successful completion of the program, verified digital certificates will be emailed to participants, at no additional cost, with the name used when registering for the program
All certificate images are for illustrative purposes only and may be subject to change at the discretion of MIT xPRO
Frequently Asked Questions (FAQs)
The RAG and Context Engineering: Designing and Building Production-Grade AI Systems program from MIT xPRO focuses on building retrieval‑aware LLM systems. The eight-week live online curriculum teaches you how to design, evaluate, and deploy RAG architectures that produce accurate and reliable outputs grounded in external knowledge sources.
MIT xPRO’s RAG and Context Engineering program is intended for technical professionals who want to build or oversee production-grade LLM systems with various retrieval methods and move beyond basic knowledge of simple prototypes.
The program includes live online sessions and virtual guided labs along with structured modules, hands-on programming assignments, and other applied learning components. It therefore differs from learning at your own pace.
In this eight-week learning journey, you will learn RAG system design end to end, along with information retrieval strategies, how to compare retrieval methods, vector search, embedding models, semantic search, evaluation frameworks, and RAG applications.
The RAG and Context Engineering program covers how embedding models and word embeddings enable vector search and improve the retrieval of relevant information.
The program explores how vector databases are used to store and retrieve relevant documents efficiently in RAG systems.
The program’s hands-on capstone project requires you to build a retrieval-aware LLM system, applying your learnings on how to connect LLMs with knowledge bases, retrieve documents, construct retrieved context, improve response quality, and design systems that generate relevant responses in real-world scenarios.
You will work with realistic datasets and practical system design scenarios, building and evaluating retrieval-based systems that use structured data and external context. Throughout the program, you will apply these concepts to domain-specific use cases, including internal tools and domain-specific chatbot systems that generate accurate, context-aware responses.
The program learning journey includes building systems with production-ready components, exploring model selection, and working with open-source LLMs and API-based access to models. You will also learn how RAG becomes a core part of scalable LLM applications. Throughout the program, you will explore how to design and evaluate LLM-powered systems using production-relevant concepts, including model selection, cost and performance trade-offs, and different approaches to accessing models (e.g., APIs and open-source options). You will also examine how RAG supports building more reliable and adaptable LLM applications.
Financing Options
Climb Credit*
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Flexible Payment Options For All
Flexible payment options allow you to pay the program fee in installments. Click here to see payment schedule.
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