context-budget-planner

Optimize token budget allocation by inventorying current usage, identifying waste, and applying a 5-zone model to distribute tokens across system prompts, RAG contexts, and conversations. This produces a balanced before/after budget report with cost impact.

Manage token limits for RAG pipelines and multi-turn conversations by applying predefined allocation profiles that distribute tokens across retrieved context, conversation history, and output reserve to maximize useful information density.

A 5-zone context window allocation model divides tokens across System Instructions, Persistent Knowledge, Retrieved Context, Conversation History, and Output Reserve, applying zone-based allocation and compression strategies to fit within model limits.

You can use predefined token allocation profiles for Chatbot, RAG Q&A, Code Agent, Document Analysis, Multi-turn Planner, and Creative Writer use cases to quickly distribute tokens across context zones based on specific task requirements.

Reduce context window waste and compress LLM prompts by identifying wasted tokens during the inventory step, then applying specific compression strategies per zone to maximize useful information density within the available context limit.

Plan a token budget when designing system prompts, RAG pipelines, or multi-turn agents that must fit within a model's context limit, ensuring balanced allocation across instruction, knowledge, retrieval, history, and output zones.