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Project P2
Context Window Dashboard
Concept: Token Budgeting & Prompt Truncation
Problem Statement
Multi-turn chatbots frequently exceed maximum token constraints, causing API errors or losing conversation history abruptly.
What Concept It Teaches
It teaches sliding window memory algorithms, conversational token budgeting, and RAG context trimming.
Why This Matters
Managing context budgets dynamically optimizes prompt scaling, reduces latency, and saves execution costs.
System Architecture
A dashboard showing total token allocation, system overhead, and dynamic chat history truncation sliders.
Chat History InputHistory Truncator ModelToken Count CalculatorMemory Diagnostics Panel
Execution Data Flow
- 1. System calculates current system prompt token overhead.
- 2. User chat messages are appended sequentially.
- 3. Truncator engine warns when total budget crosses threshold.
- 4. Suggestions recommend sliding history cutoffs.
Tech Stack
Next.jsTypeScriptTailwind CSS
Implementation Plan
- 1.1. Build memory allocation charts.
- 2.2. Implement sliding window calculations.
- 3.3. Show warnings when budget constraints are violated.
Technical Interview Defense
Defense Concept:
How do you prevent context window exhaustion in multi-turn conversation agents?
Verification Audit
Repository Checked: Yes
Repository Exists: Yes
Live Demo Verified: Yes
Demo Exists: Yes