Your LLM pipeline may be sending avoidable context—such as repeated instructions, broad retrieval results, or oversized tool outputs—but the available evidence does not establish that pipelines generally waste 60% of their token budgets. Treat 60% as a hypothesis to test against your own request-level usage data. The reliable fix is to measure tokens by workflow stage, reduce one source of excess at a time, and verify that answer quality holds.
Is 60% of an LLM pipeline’s token budget really noise?
Not as a general benchmark. The sources available for this article do not establish that LLM pipelines typically spend 60% of their tokens on noise. That figure is supportable only as a measurement from a particular pipeline, with a defined method for deciding which tokens count as unnecessary. Otherwise, it is a hypothesis—not a fact about the industry.
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“Noise” is also not a provider usage category. It is an engineering judgment: context that can be removed or narrowed without materially harming the task. A long prompt is not automatically wasteful, and a short one is not automatically sufficient. Test whether each component helps the intended outcome.
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Start with provider-reported usage rather than estimating from prompt length. Token counts vary with the model, encoding, and language. OpenAI offers rough English-language estimates—about four characters or three-quarters of a word per token—but these are not substitutes for tokenizer-specific estimates or actual API usage. See OpenAI’s guide to understanding and counting tokens.
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For each request, capture the fields your provider makes available, then aggregate by workflow stage and feature. Record enough context to compare like with like:
- Model and request or feature name.
- Input and output tokens, plus cached-input and reasoning-token details when reported.
- Retrieval result count and, if available, the contribution of retrieval and tool outputs to the prompt.
- Latency and cost, using the provider’s applicable accounting conventions.
- A task-appropriate quality signal, such as correctness or evidence coverage.
Keep vendor counting conventions in mind when comparing providers: similarly named usage fields may not represent identical things. OpenAI documents input, output, cached-input, and reasoning-token concepts in its token guide. Request-level visibility can help explain a monthly bill that a single total cannot.
Where do avoidable input tokens usually enter?
Repeated instructions and conversation history
System and developer instructions, tool definitions, and conversation history can be sent again across requests. Inspect their length and whether each part is still needed for the current task. In multi-turn flows, check whether the full history is necessary or whether a carefully evaluated summary can retain the information the model needs.
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Keep stable prompt content early and request-specific content later when using a provider’s prompt caching. This ordering can make a repeated prefix eligible for cache reuse, but it does not itself reduce the number of tokens sent.
Overly broad retrieval
Retrieval can add irrelevant or duplicative chunks, especially when a query returns many loosely related passages. Set an explicit context budget and examine whether the selected evidence supports the answer. Narrow retrieval using relevance and, where freshness matters, metadata such as dates. Adjust chunk selection, top-k, filtering, or reranking only while checking that important evidence is not lost. Microsoft’s RAG prompt-engineering guidance discusses managing retrieved context for answer quality.
Tool outputs and other payloads
Inspect what tools return to the model, not just what the model writes. A tool may provide more fields, records, or text than the next step needs. Where appropriate, trim outputs to task-relevant information and enforce limits on returned results. Validate changes against representative tasks so that useful details are not removed along with the excess.
How can you reduce tokens without sacrificing answer quality?
- Establish a representative baseline. Sample normal and difficult requests across the features you care about. Save usage, latency, cost, retrieval counts, and quality results by model and workflow stage.
- Identify the largest avoidable input source. Inspect repeated prompt content, history, retrieval, and tool payloads separately. Use actual request traces to find the source rather than assuming the prompt template is responsible.
- Set a budget for context. Decide how much room instructions, history, retrieved evidence, and tool outputs may use for each task. Treat those limits as design constraints to evaluate, not universal token values.
- Make one targeted change. For example, narrow retrieved chunks or remove an unused portion of a repeated instruction. Keeping changes isolated makes it easier to tell what affected usage or quality.
- Compare the same task set before and after. Check input tokens, cached usage, total cost, latency, correctness, and evidence coverage. Keep the change only if the token or cost benefit is useful and quality remains acceptable.
A lower token count alone is not proof of a better pipeline. A retrieval change that cuts context but drops needed evidence may reduce usage while making answers worse. Likewise, a longer answer may increase output tokens even if input handling improves.
Does prompt caching reduce token use?
Not in the sense of removing tokens from the request. OpenAI describes the feature this way: “Prompt caching reuses work when requests share the same prompt prefix.” It can reduce cost and latency for eligible repeated prefixes, while new suffix content still has to be sent. See the OpenAI prompt caching guide.
Eligibility, minimum prompt length, model support, and cache pricing depend on the model and configuration; a stable prefix or persistent session does not by itself guarantee a cache hit. Check actual cached-token usage in provider-reported data. OpenAI’s documentation, checked on October 7, 2026, states that supported model and configuration cases can receive cached-input discounts of up to 95%. That is a documented maximum, not a typical realized saving, a promise for every request, or a reduction in token count; rates and eligibility can change.
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For implementation, put stable shared content before variable request content when the provider’s cache behavior rewards matching prefixes, then confirm that requests are actually receiving cached usage. OpenAI’s latency optimization guidance also covers prompt caching and other latency considerations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which changes reduce tokens, and which mainly change cost?
| Approach | What changes | What to verify |
|---|---|---|
| Remove unnecessary instructions, history, or payload fields | Can reduce input tokens sent when content is actually removed. | Input usage and task quality on the same representative requests. |
| Narrow retrieval or tool results | Can reduce context tokens when fewer or shorter results are included. | Correctness and evidence coverage, as well as input usage. |
| Use prompt caching for repeated prefixes | Reuses computation for eligible matching prefixes; does not remove the new request’s suffix tokens. | Cache eligibility, cached-token usage, latency, and cost under the current model’s terms. |
For provider-specific advice on balancing cost and model capability, consult the current Anthropic guide to optimizing for cost and intelligence. Any comparison of optimizations should consider actual tokens removed, cost and latency impact, quality and evidence retention, and the implementation work required.
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How should you track the results over time?
Keep request-level records tied to prompt or pipeline versions so that changes can be compared across models, users, sessions, and tasks. Set thresholds for unusual usage or cost where that fits your operations, and review quality measures alongside efficiency metrics. This makes regressions easier to spot than relying on a monthly aggregate alone.
An observability platform is optional, not a prerequisite for fixing token waste. Langfuse documents tracking usage and cost for generations and embeddings, dashboards and threshold alerts, and analysis of cost, latency, quality, and volume. Its token and cost tracking documentation and metrics and analytics documentation describe one implementation example. The documentation also notes that some reasoning-model cost calculations depend on ingested usage rather than inference from text alone.
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