AVAILABLE FOR WEB, FLUTTER & QA PROJECTS
How to use this tool
1. Distribute Prompt Components
Enter or paste system instructions, few-shot examples, retrieved context, and the user query into designated fields.
2. Inspect Component Weights
Review individual token counts and percentages for each prompt segment to isolate sources of token bloat.
3. Choose Foundation Model
Select your deployment model (GPT-4o, Claude 3.5 Sonnet, Gemini 1.5) to view cost per inference and context window consumption.
4. Copy Full Prompt or Stats
Copy the combined structured prompt payload or export the token budget breakdown for architectural planning.
Formula or logic used
Multi-Part Prompt Token Allocation Logic
Production AI systems do not send raw text; they serialize structured JSON message arrays with delimiter tokens. System prompts and grounding context typically consume 70-85% of total input tokens.
Total Input Tokens = System Tokens + Examples Tokens + Context Tokens + User Query Tokens + Delimiter Overhead (~4 tokens/message)Prompt Bloat Ratio = ((System Tokens + Context Tokens) / Total Input Tokens) × 100Available Output Buffer = Model Context Limit - Total Input TokensTotal Request Cost = (Total Input Tokens × Input Rate) + (Reserved Output Tokens × Output Rate)
Examples
Example 1: Enterprise Customer Agent Prompt
Separating components clearly demonstrates that grounding context dominates the token budget.
Example 2: Autonomous Code Refactoring Agent
Alerts engineers to raise output buffers or move to a 32k/128k context model to prevent truncated code blocks.
Common use cases
System Prompt Compression
Audit verbose guidelines and eliminate duplicate instructions to reduce latency and token bills.
RAG Retrieval Context Budgeting
Determine how many retrieved knowledge chunks you can inject before crowding out the generation space.
Few-Shot Example Optimization
Measure the exact token cost of in-context learning examples versus zero-shot prompting.
Time-to-First-Token (TTFT) Improvement
Minimize initial prompt size to accelerate streaming response start times for end users.
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Frequently asked questions
How do message delimiters affect prompt token counts?
Chat models (like OpenAI GPT-4o and Anthropic Claude) wrap each message in special formatting tokens (e.g. `<|im_start|>system...<|im_end|>`). Each message role typically incurs an overhead of 3 to 5 additional tokens beyond the visible text.
What is an acceptable prompt bloat ratio?
In general conversational chatbots, system prompts should stay under 500 tokens. In RAG architectures, retrieval context usually comprises 60% to 80% of total tokens, which is expected as long as the retrieved snippets are relevant.
Can I cache system prompts to save money?
Yes. Both Anthropic (Prompt Caching) and OpenAI (Cached Inputs) offer discounts of up to 50-90% on input tokens if the prefix (system prompt and static examples) remains identical across requests and exceeds minimum cache thresholds.
What happens when a prompt exceeds the context window?
If total input tokens plus requested output tokens exceed the model's maximum context limit, the API returns an immediate 400 Bad Request error (context_length_exceeded) and refuses inference.
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