Most token-management advice is prose you read once and forget. A checklist is different — it is a tool you return to, run against a feature, and tick off item by item. This article is built to be used that way. Each item is concrete enough to act on and carries a one-line justification so you understand why it earns a place rather than treating the list as ritual.
The checklist is organized into stages that follow the natural order of work: measurement first, then allocation, then compression, then enforcement and review. You can run the whole thing against a feature in an afternoon, or use individual sections when you suspect a specific problem. Either way, the value comes from actually checking the boxes, not from reading them.
Copy this into your team's documentation and run it whenever you ship a new LLM feature or audit an existing one. The items that fail are your work queue.
A word on how to use it well. A checklist is only as good as the honesty you bring to it. It is tempting to tick a box because you remember doing the thing once, months ago, in a different version of the code. Resist that. Check the box only when you have just confirmed the item is true in the current system. The whole value of a checklist is that it catches the things that quietly stopped being true while nobody was looking, and that only works if each tick reflects a fresh verification rather than a memory.
Measurement Checklist
You cannot manage tokens you have never counted, so measurement comes first.
Items
- Count tokens with the provider's tokenizer, not word-count estimates. Estimates are unreliable for code and non-English text.
- Log token counts per component, not just a total. The total flags a problem; the breakdown locates it.
- Record output token counts and their distribution. Output is usually the pricier side and the most prone to runaway length.
- Attribute token usage to the triggering feature. A cheap-looking request can be your largest line item once multiplied by traffic.
The reasoning behind starting here is developed in Spending Tokens Like Money: A Working Manual for LLM Budgets.
Allocation Checklist
A deliberate budget beats an accidental one every time.
Items
- Reserve output space before allocating input. Output overflows the window if left unbudgeted.
- Set an explicit token budget for each input component. Unbudgeted components grow until something breaks.
- Rank components by the value they add to answers. The user message and core instructions outrank history and retrieval.
- Confirm input plus reserved output fits the context window. Exceeding it causes failures or silent truncation.
This allocation discipline mirrors Token Budget Management and Optimization: Best Practices That Actually Work.
Compression Checklist
Cut tokens the model did not need, not tokens it did.
Items
- Summarize older conversation turns instead of truncating. Summaries preserve decisions and facts in fewer tokens.
- Keep the most recent turns verbatim. Immediate context needs full detail to stay coherent.
- Chunk and rerank retrieved documents; include only top passages. Focused context is cheaper and often improves answers.
- Strip boilerplate, navigation, and repeated headers before sending. You are paying for every byte of cruft.
- Audit the system prompt and remove instructions that no longer change behavior. Its waste is multiplied across every request.
- Prefer structured responses over free-form prose. Structure is usually shorter and more useful.
A sequential version of these cuts appears in Cut Your Token Costs This Afternoon: An Ordered Routine.
Enforcement Checklist
A limit not enforced is a wish, not a budget.
Items
- Cap maximum output length in the API call. Prevents the most common runaway-cost problem.
- Enforce every component cap in code at prompt assembly. Intentions drift; enforced limits hold.
- Centralize all limits in one configuration location. Scattered limits are invisible and untunable.
- Degrade gracefully when a limit is hit — summarize, drop lowest-ranked context, or return a clear error. Better than crashing or silently losing important content.
Review Checklist
Budgets decay without periodic attention.
Items
- Re-run the measurement checklist on a regular cadence. Usage drifts and new prompts accumulate.
- Flag any feature whose cost grew faster than its usage. That gap signals creeping waste.
- Re-verify answer quality after any token reduction. A reduction that degrades answers is not a win.
- Revisit caps as models and pricing change. Last year's budget may no longer be optimal.
The full review loop in practice is shown in Case Study: Token Budget Management and Optimization in Practice.
Putting the Checklist to Work
A checklist that lives in a document nobody opens does no good. The teams that benefit from this one wire it into the moments where token decisions actually get made.
Attach It to Code Review
Add the enforcement items to your pull-request template for any change that touches prompt assembly. A reviewer who sees an uncapped output length or a limit hard-coded outside configuration can catch the problem before it ships rather than after it shows up on a bill. Making the checklist part of review turns it from an occasional audit into a continuous guardrail.
Make Failures Visible
When you run the checklist against a feature, record which items failed and where. A short list of failures, attached to the feature, becomes a shared work queue instead of a private observation that evaporates by the next sprint. Visibility is what turns a finding into a fix.
Re-run After Major Changes
A model upgrade, a pricing change, or a significant feature rewrite can invalidate budgets that were correct yesterday. Treat any of those events as a trigger to re-run the relevant sections. Budgets are not set once; they are maintained, and the checklist is the maintenance routine.
Keep a Per-Feature Record
For each LLM feature, keep a short record of when the checklist was last run and which items passed. This turns the checklist from a momentary exercise into a maintained ledger. A feature that has not been checked in months is itself a flag, regardless of its current numbers, because so much drifts in that span. The record also makes it easy to see, across many features, which ones are overdue for attention.
Adapting the Checklist to Your Stack
No two systems are identical, and a checklist applied mechanically to the wrong context wastes effort. A few adjustments keep it relevant.
Weight the Sections to Your Feature Type
A stateless classifier has no conversation history, so the compression items about summarizing turns simply do not apply, and forcing them only adds noise. A long-running assistant, by contrast, lives and dies by those items. Read the feature first, then weight the sections accordingly. The point is deliberate consideration of each item, not blanket application.
Add Items You Learn the Hard Way
This list captures common failure modes, but your system will teach you its own. When a token problem surprises you, distill it into a new checklist item with a one-line justification and add it. A checklist that grows with your team's scar tissue stays useful long after the generic version would have gone stale. The instinct to convert lessons into reusable structure is the same one behind The RAACE Model: A Repeatable Way to Budget Tokens.
Frequently Asked Questions
How often should I run the full checklist?
Run it whenever you ship a new LLM feature and on a regular cadence for existing ones, monthly being reasonable for active features. Also run it immediately when a feature's cost grows faster than its usage.
Which section should I prioritize if I am short on time?
Measurement and enforcement. Measurement tells you where the problem is, and enforcement — especially capping output length — fixes the most common and expensive issues quickly.
Why include a justification for each item?
So the checklist is understood rather than performed by rote. Knowing why an item matters helps you adapt it to your context and decide when an item genuinely does not apply.
Can I skip items that do not apply to my feature?
Yes. A classifier with no conversation has no history items to check. The point is to consider each item deliberately, not to force every one onto every feature.
How do I keep the checklist results from going stale?
Treat failed items as a work queue, fix them, and re-run the checklist on your regular cadence. Centralized, enforced limits are what keep the results from drifting back.
Key Takeaways
- Run measurement first: count with the real tokenizer, log per component, and attribute cost to features.
- Allocate a deliberate budget, reserving output space and ranking input components by value.
- Compress by summarizing history, reranking retrieval, pruning the system prompt, and preferring structure.
- Enforce every cap in code and configuration, and degrade gracefully when limits are hit.
- Review on a cadence, re-verifying quality and flagging any feature whose cost outgrew its usage.