Prompt, Context & RAG Hub
Estimate Byte-Pair Encoding subword token counts, plan RAG chunking and vector retrieval budgets, evaluate vision token consumption, and model conversation memory growth.
How tokens and context windows work
Before you can budget an AI feature, you need to know how much text a model actually sees. Models do not count words the way people do. They split text into tokens, small subword pieces produced by a process called Byte-Pair Encoding. Common English words may become a single token, while code, numbers, and less common languages split into several. That is why two prompts that look the same length on screen can cost very different amounts.
The Prompt Token Estimator turns your own text into an estimated token count. Paste a real prompt, including system instructions and any retrieved context, and it estimates tokens using the content-type ratios the tool documents. This matters because most cost surprises come from context nobody counted: a long system prompt, a retrieved document, or a conversation history carried into every new request.
The Context Window Calculator answers the next question: how much can a model hold at once? It converts a context window size into words, pages, and lines of code, so an abstract number like 128,000 tokens becomes something you can plan around. A window that holds roughly 96,000 words sounds generous until you load several long documents, a full conversation, and still need room for the answer itself.
Planning a prompt is therefore a budgeting exercise. Reserve space for the output, not just the input. Decide what must stay in context for every request and what can be summarised, retrieved on demand, or dropped. Shortening a repeated system prompt by a few hundred tokens saves a little on one call and a lot across thousands of calls, especially when output tokens are priced higher than input tokens.
The seven tools on this hub cover the complete lifecycle of model context and knowledge retrieval. Use the Prompt Token Estimator and Context Window Calculator for subword ratios and capacity limits. Model knowledge retrieval with the RAG Token Budget and RAG Chunking calculators, forecast index storage with the Embedding & Vector Storage calculator, calculate image patch consumption with the Vision Image Input Tokens calculator, and project multi-turn compounding with the Conversation Token Growth calculator. Used together, they replace guesswork with a measured starting point for any AI feature.
Calculators in this Cluster
Estimate token usage from natural language text and plan context buffer allocation:
- Standard English conversational prose averages roughly 1.33 tokens per word (4 characters per token).
- RAG retrieval chunks often comprise 60% to 80% of total prompt input tokens in question-answering pipelines.
- A 128k context window holds roughly 96,000 words, equivalent to a 300-page business book or 12,800 lines of code.
Inference Cost Calculators Fed by Token Counts
Once token counts and context boundaries are estimated, translate those volumes into dollar expenditures: