Use less repeated context, not less useful context. Keep automatically loaded instructions short and stable, request only the tool data needed for the immediate decision, and save durable progress in a compact state note instead of carrying an entire transcript into every session. Use CLI continuation, resume, and turn limits to control workflow scope, then check current usage records and billing documentation because token totals and billed cost are different measures.
What you are actually trying to reduce
A Claude Code session spends context on everything the model can see: instructions loaded at startup, your messages, previous turns, tool responses, and any files or excerpts you include. Some of that context is essential to solving the task. Some is repeated policy, stale investigation, broad tool output, or a transcript that records decisions nobody needs to reread.
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The useful target is therefore unnecessary context, not the shortest possible prompt. Removing a constraint, an acceptance test, or a key architectural decision can make a task cheaper to read but more expensive to redo. The practical objective is to preserve the information that changes a decision while dropping material that does not.
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| Workflow choice | Lower-context approach | What you must preserve |
|---|---|---|
| Instructions | Automatically load concise, stable guidance; retrieve detailed task material only when needed | Project rules, safety constraints, conventions, and durable decisions |
| Tools | Ask for filtered records, fields, files, or excerpts | The evidence required for the current question |
| Long jobs | Carry a short external state note between sessions | Goal, decisions, completed work, open issue, and next action |
| Automation | Give each run a defined scope and turn limit | Completion criteria and a way to continue if the limit is reached |
Anthropic’s documentation does not establish a universal percentage reduction for these practices. Their value is operational: fewer irrelevant tokens leave more room for the code, evidence, and constraints that matter.
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Keep automatically loaded instructions focused
Anthropic’s memory guidance says Claude Code automatically loads memory files into context. That makes a project-level or personal CLAUDE.md useful for information that applies broadly and remains true across tasks. It also means every paragraph placed there can become recurring context.
Put stable rules in the always-loaded file
A good always-loaded file answers questions Claude Code is likely to face on nearly every task:
- Which language versions, package managers, and test commands the project uses.
- Directory conventions and boundaries, such as generated files that must not be edited.
- Required checks before a change is considered complete.
- Security, privacy, licensing, or deployment constraints that apply to all work.
- Short definitions of project-specific names that otherwise cause repeated confusion.
Write each rule as a decision aid. “Run the unit suite before opening a pull request” is more useful than a page explaining the history of the test suite. Remove obsolete migration notes, old incidents, and examples that no longer describe the repository.
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Anthropic’s memory page describes automatic loading; it does not promise a measured token saving from any particular file layout. Moving detailed material into files that Claude Code reads only when a task needs it is a reasonable inference from that automatic-loading behavior, not a documented percentage reduction.
Keep design proposals, API inventories, release checklists, incident timelines, and component-specific instructions in separate, clearly named documents. The always-loaded file can point to their purpose in a sentence, while the task prompt identifies which document to open. This preserves discoverability without paying the context cost of every document on every session.
Review the file like a budget
On a regular cadence, inspect the default instruction file and ask of each paragraph:
- Does this affect most tasks?
- Is it still true?
- Could it be expressed as one short rule?
- Would a link to a local document be enough until a specialist task needs the detail?
Delete duplicated rules rather than adding an exception below them. Contradictory instructions force clarification turns, which can consume more context than the original prose.
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Make tool and MCP responses answer-shaped
Large tool responses compete with the task itself for context. Anthropic’s MCP documentation warns about large outputs; it does not provide a precise current threshold that can be treated as a token-saving rule. The safe practice is to request the smallest response that can support the next decision.
Ask for the exact slice
Instead of asking a tool to dump an entire table, repository, log, or document, specify:
- The records or files relevant to the question.
- The fields needed for the decision.
- A time range, status, owner, or other filter.
- An excerpt or maximum result count when a full record is unnecessary.
- Whether you need raw data, a summary, or both.
For example, a debugging request can ask for error entries from the last deployment window with timestamps, request identifiers, and stack traces, rather than every log line. A data investigation can request the columns needed to test a hypothesis rather than the complete schema and all rows.
Separate discovery from retrieval
When you do not know where the relevant material is, use a two-stage pattern. First ask for names, identifiers, or a compact index. Then retrieve only the matching records or files. This avoids repeatedly sending a broad dump while you refine the question.
Paginate deliberately
If the answer genuinely spans many records, process it in bounded pages and keep a running note of what has been covered. Ask the tool for the next page only after deciding what the previous page established. A page size is a workflow choice, not a universal Anthropic limit; select one that leaves room for reasoning and follow-up.
Do not throw away evidence you still need
Filtering is harmful when it removes the field that explains a discrepancy. Before narrowing a response, identify the acceptance test: which value, line, or relationship would change your conclusion? Request that evidence explicitly. If the result is ambiguous, retrieve a focused follow-up rather than restoring the entire original dump.
Preserve progress outside the conversation
For work that spans sessions, keep a concise state note in the project. It should be readable by a future session without replaying every turn.
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Use a five-part state note
Goal: the outcome this work must deliver
Decisions: choices already made and why they are binding
Completed: changes, tests, and investigations finished
Open issue: the specific uncertainty or blocker
Next action: the smallest concrete step to resumeUpdate the note after a meaningful decision, a completed test, or a change in scope. Record the result, not the conversation that produced it. Include file paths, identifiers, and commands when they are necessary to reproduce the state, but omit exploratory dead ends unless they prevent someone from repeating a failed approach.
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Start the next session with recovery information
Give Claude Code the goal, point it to the state note, and state the next action. Ask it to verify the note against the current files before making changes. If the repository has moved on, update the note rather than blindly trusting an old conclusion.
Anthropic’s prompting best-practices page says: "If you are using Claude in an agent harness that compacts context or allows saving context to external files (like in Claude Code), consider adding this information to your prompt so Claude can behave accordingly." The practical implication is to make the durable state explicit. Do not assume compaction preserves every detail, and do not treat that prompting page as a current command reference for Claude Code.
Keep one source of truth
A state note works only when it does not diverge from the code. After a refactor, update paths and decisions immediately. If several notes exist, designate one current handoff note and archive older ones. This prevents future sessions from loading multiple competing histories.
Scope CLI sessions instead of letting them sprawl
Anthropic documents CLI controls for continuing work, resuming a session, and capping non-interactive turns. These controls shape how a workflow runs; they are not documented guarantees that the task will require fewer input tokens.
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Continue an active session when the same files, constraints, and immediate objective remain in view. This avoids rebuilding setup and can preserve decisions that have not yet been written elsewhere. Before continuing, remove or summarize exploratory tool output that no longer affects the next step.
Resume from a state note when the session is stale
Resume a task through the CLI’s documented session mechanism when you need continuity, but prefer a fresh, focused handoff if the earlier conversation contains many irrelevant branches. A resume feature can recover history; it does not make every historical token necessary for the next decision.
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Cap non-interactive turns for bounded work
A maximum-turn setting is useful for jobs such as “inspect these files, run the relevant checks, and report failures.” Define what counts as completion and what should happen when the cap is reached. A cap can prevent an automation loop from expanding indefinitely, but a lower cap may stop before the fix is validated. Treat it as a reliability and scope control, not a savings claim.
| Situation | Preferred workflow | Reason |
|---|---|---|
| One focused change with known files | Continue the current session and keep tool requests narrow | Existing constraints are still relevant |
| Work resumed after a break | Use a state note and verify the repository before editing | Durable facts matter more than transcript length |
| Automated inspection or batch task | Set an explicit scope and documented maximum turns | Prevents unbounded wandering while preserving a stop condition |
Distinguish token totals from billed cost
Counting tokens and estimating a bill answer different questions. Anthropic’s pricing documentation describes input usage that can include ordinary input tokens, cache creation, and cache reads. It also describes model-specific conditions for long-context billing. Rates, thresholds, and model eligibility can change, so stale figures should not be used as a current cost estimate.
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A workflow can reduce repeated context while still showing substantial input usage because a large task genuinely requires code and evidence. Conversely, caching can affect billing without reducing the amount of information represented in a request. Check the current usage records for the account and the current pricing page for the model and context conditions you are using.
Do not treat a subscription as a token-saving method
Anthropic lists Claude Pro and Max as access routes for Claude Code. Choosing a subscription changes access and billing arrangements; it is not evidence that the workflow uses fewer tokens. Compare plans only for the access, limits, and pricing terms currently published by Anthropic, not as a substitute for context hygiene.
A repeatable low-waste workflow
- Define the deliverable. State the files, behavior, tests, or decision that will mark the task complete.
- Load only durable defaults. Keep broadly applicable project rules in the automatically loaded memory file; place specialist instructions in task-specific documents.
- Start with a small evidence request. Ask for the relevant files, records, fields, or excerpts, with filters and a time range.
- Expand selectively. If the evidence is insufficient, request the one additional field, file, or page that could change the conclusion.
- Record decisions. Update the state note with the goal, decisions, completed work, open issue, and next action.
- Choose session control. Continue while the active context remains useful; resume from the handoff when it does not; cap automated turns when the task has a bounded scope.
- Verify the outcome. Run the required checks and record their results before closing the session.
- Inspect actual usage. Use current account records and pricing documentation to understand tokens, cache activity, and billed cost.
Common failure modes and fixes
The memory file becomes a project encyclopedia
Symptom: Every task loads history, long tutorials, and obsolete exceptions.
Fix: Keep only stable rules and concise definitions in the default file. Move design history and specialist procedures to on-demand documents, and remove material that no longer changes decisions.
A tool returns far more data than the question needs
Symptom: The model spends turns locating relevant lines in a complete export.
Fix: Repeat the request with a filter, field list, identifier, date range, or excerpt size. Preserve the specific evidence needed to test the hypothesis.
The handoff note is too vague
Symptom: A new session has to rediscover what was tried and what remains.
Fix: Replace narrative with a concrete goal, binding decisions, completed checks, one open issue, and one next action. Include exact paths or identifiers where they matter.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsA turn cap stops useful work
Symptom: Automation ends before tests or verification run.
Fix: Define completion before setting the cap, reserve turns for validation, and make the stop message point to the state note so the next run can continue. Do not lower the cap solely to claim token savings.
A lower bill is assumed from a lower-looking token count
Symptom: An estimate ignores cache creation, cache reads, model-specific long-context rules, or current pricing.
Fix: Compare the usage record and billing terms for the exact model and period. Keep workflow optimization and price analysis as separate checks.
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Spend context on constraints, evidence, and decisions that can change the result. Keep recurring instructions concise, make tool responses answer-shaped, preserve progress in a small external note, and bound automation with an explicit objective. These practices improve recoverability and focus without promising a universal token-saving percentage; only current usage records and pricing documentation can show what they changed for your account.
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