What's in the Context Window: A Field Guide to Context Engineering
What's really inside an AI model's context window, and why does it matter for building reliable enterprise AI applications? As organizations move beyond basic prompt engineering toward sophisticated generative AI and agentic systems, context engineering is becoming an essential discipline for improving model performance, accuracy, and reliability. In this session, "What's in the Context Window: A Field Guide to Context Engineering," from COLLIDE Data + AI Conference 2026, Jessica Emmons, Sr. Solution Architect @ Redis, explores the technical considerations behind managing the information that large language models (LLMs) use to generate responses and execute complex workflows. A context window represents more than the amount of text an LLM can process. Effective context engineering involves determining which instructions, retrieved documents, conversation history, tool outputs, and relevant enterprise data should be available to a model at a given moment. For AI engineers, data architects, and enterprise technology leaders, managing context effectively raises important questions about retrieval quality, token efficiency, latency, relevance, and scalability. As AI applications become more dynamic and autonomous, designing the right context management strategies can be critical to building systems that deliver consistent, grounded, and useful responses. This session offers an opportunity to explore the evolving practice of context engineering and its role in production-ready generative AI applications. 🎯 Key Topics to Explore: Context Engineering Fundamentals: Understanding the information and instructions that shape how LLMs interpret requests and generate responses. Context Windows and Token Management: Why context limits, information prioritization, and token efficiency matter for AI application performance. Retrieval-Augmented Generation (RAG): How retrieving relevant information can help ground AI responses in enterprise knowledge. Memory and Context Management: Considerations for maintaining conversation history, application state, and relevant information across agentic workflows. Production AI Performance: The relationship between context quality, retrieval strategies, latency, and reliability in enterprise AI systems. 🎤 Featured Speaker Jessica Emmons Sr. Solution Architect | Redis Session Brought to You By: Redis About COLLIDE Data + AI Conference 2026 Organized by Data Science Connect, COLLIDE Data + AI Conference 2026 took place on October 1, 2026, at the Sandy Springs Performing Arts Center in Atlanta, Georgia. The conference brought together enterprise decision-makers, data and AI executives, technology practitioners, and leading AI solution providers to explore the evolving data and AI landscape, scale AI responsibly, and exchange practical insights with industry peers. Learn more about COLLIDE: https://datasciconnect.com/events/collide/ ▶️ Watch More from COLLIDE 2026 Watch the full session to explore the principles of context engineering and the considerations behind building more reliable, context-aware enterprise AI applications. Subscribe to the Data Science Connect YouTube channel for more technical presentations, expert discussions, and executive keynotes from COLLIDE Data + AI Conference 2026 covering generative AI, context engineering, agentic AI, and enterprise AI architecture.