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Documentation Index

Fetch the complete documentation index at: https://docs.axiomancer.io/llms.txt

Use this file to discover all available pages before exploring further.

The Optimize page runs a recurring pass over the last 7 days of request_logs and produces concrete findings — each with an estimated monthly $ impact and a one-click fix path.

Finding types

FindingWhat it detectsTypical fix
Oversized system promptSystem prompts above a per-model size threshold that don’t measurably improve output quality.Trim the prompt or cache the system block.
Duplicate requestsIdentical or near-identical prompts repeated within a short window from the same key.Add idempotency or a client-side cache.
Model mismatchTasks classified as chat running on frontier models, or agentic running on small models.Re-route via a rule.
Underused budgetKeys with monthly budgets that haven’t been touched in 30+ days.Lower the budget or revoke.
Stuck in retry loopSessions with retry rate > 50% on the same model.Compare against a stronger model in /models/compare.

How estimates are calculated

Each finding ships with a projected monthly $ savings, computed from:
  • The volume of matching requests in the last 7 days.
  • The cost delta between the current model and the suggested target (priced from the LiteLLM catalog).
  • A 30-day projection assuming current traffic patterns hold.
Estimates are conservative — RouteShift floors negatives to zero and ignores findings under $1/month so the page only surfaces work worth doing.

Acting on a finding

Each card has an Apply fix button that drops you into the right surface:
  • Oversized prompt → links to the offending requests so you can see which prompt template needs trimming.
  • Duplicate requests → opens the rule editor pre-filled to add a deduplication tag.
  • Model mismatch → opens /models/compare with both models pre-selected, then opens a rule draft.
  • Stuck in retry → same as model mismatch.
Findings auto-resolve when the underlying behavior disappears from the trailing 7-day window.