AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Select Model Context Target
Set total token budget (e.g. 8k, 16k, 32k, or 128k tokens).
2. Allocate Fixed System & Memory Tokens
Assign tokens for system instructions, tool definitions, and multi-turn chat memory.
3. Budget RAG Retrieval & Output Space
Allocate token budget for retrieved passages and reserve sufficient generation space.
4. Inspect Visual Budget Allocation
Review percentage distribution bar, identify bottlenecks, and export token budget configuration.
Formula or logic used
Context Token Partitioning Formulas
A well-architected RAG prompt partitions available context into deterministic budget slices to ensure generation headroom is never crowded out.
Total Context Budget: $B_{\text{total}} = B_{\text{system}} + B_{\text{history}} + B_{\text{rag}} + B_{\text{query}} + B_{\text{output}}$Available RAG Space: $B_{\text{rag}} = B_{\text{total}} - (B_{\text{system}} + B_{\text{history}} + B_{\text{query}} + B_{\text{output}})$Max Retrievable Chunks: $N_{\text{chunks}} = \lfloor \frac{B_{\text{rag}}}{\text{Avg Chunk Tokens}} \rfloor$Recommended Split: 10-15% System, 20-30% History, 40-50% RAG Context, 15-20% Output
Examples
Example 1: Standard Enterprise Assistant (16,000 Token Target)
Balanced distribution ensuring fast inference while leaving room for multi-turn history.
Example 2: Compact Low-Cost Bot (4,000 Token Budget)
Optimized for extreme cost efficiency and sub-second first-token response times.
Common use cases
Dynamic Sliding Window Sizing
Programmatically compute how many conversation turns to retain before trimming chat history.
Preventing Truncation Errors
Guarantee that the model always has enough reserved output tokens to finish generating JSON or code.
Multi-Agent Message Routing
Budget message payloads passed between collaborating autonomous agents in a LangGraph workflow.
Cost Control Governance
Enforce strict per-query token ceilings across enterprise developer teams.
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Frequently asked questions
Why is reserving output headroom critical?
If prompt tokens consume 99% of the context window, the model runs out of token capacity mid-sentence and returns an incomplete, broken response or malformed JSON.
How should I handle chat history when budget runs out?
Implement a sliding window (keep only last N messages), or run an asynchronous background summarization prompt to condense older conversation turns into a short paragraph.
What percentage of context should RAG documents occupy?
In typical RAG applications, retrieved context should occupy 40% to 60% of the active context budget to provide sufficient grounding without crowding out history or instructions.
Can I dynamic-size RAG chunks based on remaining budget?
Yes! Many production architectures query the vector database and add chunks one by one until the cumulative token count reaches the allocated RAG budget ceiling.
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