AI Lesson & Submodule
Context Budget Management
Learn chat history growth, system prompt overheads, and token constraints.
Why This Matters
In multi-turn chat applications, history token usage grows exponentially. Active budget management prevents early failures.
Deep-Dive Explanation
Managing token budgets requires dynamically tracking the length of system prompts, user queries, retrieved RAG context, and active conversation history. If the combined token length approaches the model's limit, the application must apply compression, truncation, or history-trimming policies. This prevents context exhaustion API errors and maintains low latency.
What You Will Learn
- •Tracking session history tokens inflation
- •Protecting slots for system instructions
- •Setting threshold safety margins
Concepts Covered
Chat History Token GrowthSystem Prompt AllocationsSafety Thresholds
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