Back to Module 1.2: Context EngineeringIn Progress
AI Lesson & Submodule
What is a Context Window?
Explore model memory capacities, input/output limits, and token budgets.
Why This Matters
A model context window is a hard limit. Exceeding it throws API errors, while fill bounds degrade retrieval accuracy.
Deep-Dive Explanation
The context window is the maximum sequence length (input + output tokens) that a model can process in a single inference step. In standard transformer architectures, the self-attention layer computes relationship values between every pair of tokens. This results in quadratic O(N^2) time and space complexity, meaning that doubling the sequence length quadruples the GPU memory and processing steps required.
What You Will Learn
- •The architectural boundaries of model context windows
- •Separating input vs output token allocations
- •Cost math behind scaling context windows
Concepts Covered
Context CapacityToken LimitsCompute Complexity
Mapped Foundation Project: Context Window Dashboard
Diagnostic analyzer tracking chat history expansion, system prompt parameters, and memory optimization suggestions.
Architecture Preview
A dashboard showing total token allocation, system overhead, and dynamic chat history truncation sliders.
Chat History InputHistory Truncator ModelToken Count Calculator
Tech Stack Planned
Next.jsTypeScriptTailwind CSS