Context Engineering with RAG
Published by Pearson
Build production-ready AI systems using context engineering principles
- Learn the fundamentals of context engineering by building production RAG systems from scratch.
- Master context ingestion, storage, routing, and prioritization patterns that prevent AI failures.
- Practice hands-on exercises showing real-world solutions to context management challenges using Python and modern vector databases.
Context engineering is the systematic design and management of information that enables AI systems to scale intelligently. Unlike prompt engineering, which focuses on inputs, context engineering covers the full data architecture around AI models: ingestion, retrieval, and conflict resolution.
This 2-day workshop teaches context engineering through Retrieval-Augmented Generation (RAG), the leading approach for grounding AI in organizational knowledge. You’ll build pipelines, multi-tier storage, intelligent routing, and tackle real-world context challenges. Day 1 focuses on foundations: you will normalize diverse data sources, implement semantic search, design caching strategies, and create context routers. On day 2, you'll apply these principles to production RAG systems: you will manage context windows, compress long-form data, integrate multiple sources, and debug failures that cause hallucinations. Hands-on coding with real datasets will teach you to diagnose issues, measure context quality, and optimize for deployment.
What you’ll learn and how you can apply it
- Build context ingestion pipelines that validate, normalize, and enrich data from multiple sources.
- Implement vector-based semantic search and multi-level caching to retrieve context efficiently.
- Design context-aware routing systems that prioritize and resolve conflicts among competing information sources.
- Develop production RAG systems that manage context windows, handle multi-turn conversations, and incorporate real-time data.
- Troubleshoot context engineering failures and enhance system performance using measurement and A/B testing.
This live event is for you because...
- You're a software engineer or data scientist building AI-powered applications and need systematic approaches to context management.
- You're implementing RAG systems and encountering issues with irrelevant retrieval, context window constraints, or hallucinations.
- You want to move beyond basic prompt engineering to architect scalable, maintainable AI systems.
- You need practical, code-first guidance on production context engineering patterns.
Prerequisites
- Proficiency in Python programming, including working with APIs and data structures
- Basic understanding of LLMs and how to call API endpoints (OpenAI, Anthropic, or similar)
- Familiarity with fundamental concepts like embeddings and vector similarity (high-level understanding sufficient)
- Experience with at least one cloud platform or local development environment for running Python applications
Course Set-up
Required Software and Accounts:
- Python 3.9+ installed locally with pip
- Code editor (VS Code, PyCharm, or similar)
- API key for OpenAI or Anthropic (free tier sufficient for exercises)
- Git installed for cloning exercise repositories
Recommended Setup:
- 8GB+ RAM for running local vector databases
- GitHub account for accessing course materials
Course Repository:
- A GitHub repository link will be provided before the first session with setup instructions, sample datasets, and starter code for all exercises.
Optional Tools:
- Docker (for containerized vector database setup)
- Jupyter Notebook (for interactive exploration)
Recommended Preparation
Watch:Practical Retrieval Augmented Generation (RAG) by Sinan Ozdemir Attend:Context Engineering with MCP by Tim Warner Attend:GenAI Foundations, Fine-Tuning, RAG, and LLM Development by Rob Barton and Jerome Henry
Recommended Follow-up
- Watch: Practical Retrieval Augmented Generation (RAG) by Sinan Ozdemir
- Attend: Retrieval-Augmented Generation (RAG) and LLMs by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
DAY 1: Context Engineering Foundations & Implementation
Segment 1: Context Engineering in Practice (40 minutes)
- What is context engineering and why it matters: moving beyond prompt engineering
- The context problem in production systems: token limits, relevance, and consistency
- Context as data architecture: ingestion, transformation, retrieval
- Real-world context engineering failures and their root causes
- Code Demo: Same query with different context architectures showing impact on output quality
- Q&A (5 minutes)
- Break (5 minutes)
Segment 2: Context Ingestion & Normalization (40 minutes)
- Building context pipelines: from raw data to usable context
- Data validation and schema verification strategies
- Normalization techniques for heterogeneous sources
- Metadata tagging and semantic enrichment patterns
- Code Demo: Processing multiple document formats into a standardized context
- Exercise: Build a context ingestion pipeline handling JSON, markdown, and PDF inputs (20 minutes including review)
- Q&A (5 minutes)
- Break (5 minutes)
Segment 3: Context Storage & Retrieval Patterns (30 minutes)
- Vector embeddings as context representation
- Semantic search vs. keyword search: when to use each
- Context caching strategies: short-term memory, session state, and persistent storage
- Multi-tier storage architecture for production systems
- Code Demo: Implementing vector storage with Chroma and querying by semantic similarity
- Exercise: Create a multi-tier context storage system with caching (20 minutes including review)
- Q&A (5 minutes)
- Break (5 minutes)
Segment 4: Context Routing & Prioritization (30 minutes)
- Priority assessment algorithms for multiple context sources
- Route determination: matching context to downstream consumers
- Conflict resolution when contexts contradict each other
- Dynamic context weighting based on relevance and recency
- Code Demo: Building a context router handling competing information
- Exercise: Implement a priority-based context router with conflict resolution (20 minutes including review)
- Q&A (5 minutes)
Day 1 Wrap-up (5 minutes)
DAY 2: RAG as Context Engineering in Action
Segment 1: RAG Architecture Through Context Lens (30 minutes)
- RAG as applied context engineering: mapping theory to practice
- Context flow in RAG: ingestion → storage → retrieval → injection
- Chunking as context modularization: strategies and trade-offs
- Query understanding and context selection mechanisms
- Code Demo: Build a basic RAG system focused on context flow architecture
- Exercise: Implement a complete RAG pipeline with a custom chunking strategy (20 minutes including review)
- Q&A (5 minutes)
- Break (5 minutes)
Segment 2: Production Context Management (30 minutes)
- Managing context window constraints in real-world applications
- Context compression and summarization techniques for long documents
- Session state management and conversation context patterns
- Multi-turn context accumulation strategies
- Code Demo: Context window management with automatic compression
- Exercise: Build a conversation system with session-based context management (20 minutes including review)
- Q&A (5 minutes)
- Break (5 minutes)
Segment 3: Advanced Context Engineering Patterns (30 minutes)
- Multi-source context integration: combining databases, documents, and APIs
- Hierarchical context models in practice: parent-child relationships
- Context transformation for different downstream consumers
- Real-time vs. batch context updates: architecture decisions
- Code Demo: Multi-source context system with intelligent prioritization
- Exercise: Integrate three different context sources with unified retrieval (20 minutes including review)
- Q&A (5 minutes)
- Break (5 minutes)
Segment 4: Debugging & Optimization (25 minutes)
- Diagnosing context engineering failures: lost context, irrelevant retrieval, contradictions
- Measuring context relevance and quality with metrics
- Performance optimization techniques: indexing, caching, query optimization
- A/B testing context strategies for continuous improvement
- Production deployment considerations and monitoring
- Exercise: Debug and fix a broken context pipeline with multiple issues (25 minutes including review)
- Q&A (5 minutes)
Course wrap-up and next steps (5 minutes)
- Key takeaways and implementation roadmap
- Resources for continued learning
- Production patterns checklist
- Community and support channels
Your Instructors
Shivendra Srivastava
Shivendra Srivastava is an Engineering Manager at Amazon Web Services (AWS), where he leads teams building data plane services that power AWS Lambda, Athena, Glue, and Bedrock. With 17 years of experience building cloud-based solutions for large-scale B2B and B2C customers, Shivendra has established himself as a leader in software engineering and artificial intelligence. Shivendra holds a Master of Science in Computer Science from the Georgia Institute of Technology and a Bachelor of Technology in Electrical & Electronics Engineering from SASTRA University. His technical expertise spans across multiple cloud platforms, having previously served as a Senior Program Manager at Microsoft Azure, where he architected critical security and secrets management systems. A prolific contributor to the AI community, Shivendra has co-authored four papers published in the IEEE Silicon Valley Chapter's FeedForward Magazine and serves as a paper reviewer for IEEE conferences, including ITEC 2024. He has judged multiple national and international hackathons, including the University of Washington's MBA capstone projects, and serves as a Technical Advisor for UpSquad, a Tennessee-based startup.
Naresh Vurukonda
Naresh Vurukonda is an Associate Director of Information Systems at Amgen, where he leads high-performing teams delivering Machine Learning, Data Engineering, and Generative AI solutions that enable commercial excellence and improve patient outcomes. With over 13 years of experience, he has designed and deployed advanced analytics and ensemble modeling solutions across sales, marketing, and patient-centric use cases, establishing himself as a trusted leader in data engineering and applied artificial intelligence. Naresh holds a master’s degree in Computer Science from Southern Arkansas University and a Bachelor of Technology in Electrical & Electronics Engineering from Jawaharlal Nehru University. His technical expertise spans modern programming languages, cloud platforms, and large-scale data architectures. Prior to Amgen, he served as an Architect at Viacom Media Networks, where he drove enterprise-scale data and analytics initiatives. A recognized contributor to the broader technology community, Naresh has co-authored two papers published in the IEEE Silicon Valley Chapter’s FeedForward Magazine and a speaker for IEEE conferences. He has judged at national and international hackathons and currently advises UpSquad, a Tennessee-based startup, as a Technical Advisor.