token-usage-optimization

You can estimate LLM token usage and cost by sizing individual prompt components like system instructions, tool lists, history, and retrieval inputs. This approach breaks down context length to forecast budgets and identify specific areas for reduction.

The best way to reduce LLM latency and token cost is applying an optimization playbook that trims inputs, reduces retrieval size, enables prompt caching, and enforces output length caps. This prioritizes high-impact reductions across your AI tasks.

Yes, token optimization heuristics support model selection and budgeting by forecasting token counts and latency across various models. You can compare estimated costs against your target budget to select cheaper models for your tool workflows.

Token estimation works with retrieval contexts and tool-based conversations by sizing each component individually. It measures tokens for tool lists, conversation history, and retrieved documents to troubleshoot context length issues and optimize agent performance.

Your LLM prompt likely exceeds context length limits due to accumulated tokens from system instructions, tool definitions, history, and retrieval inputs. Per-component token sizing identifies which specific inputs are consuming the most context.

Yes, you need the tokenx dependency to run token usage optimization as it provides the foundational scripts and references for estimating token counts and applying the optimization playbook to your AI tasks.