How to Reduce Token Usage Without Losing Quality

Reduce input by selecting, not merely shortening

• Send the current task state instead of the full transcript.
• Retrieve relevant passages rather than entire document collections.
• Remove duplicated policies and examples.
• Reference structured fields instead of verbose prose where meaning remains clear.
• Reuse stable prompt prefixes through supported caching when the workload benefits.

Keep provenance, exceptions and acceptance criteria. A short prompt that triggers another attempt is not efficient.

Evidence & context: Lewis and colleagues, 2020 · OpenAI Developers

Ask for the output the next step can use

Specify the required fields, audience and level of detail. If a workflow needs a category and a short rationale, do not request an essay. Use an appropriate output limit as a guardrail, then test whether truncation or omitted reasoning harms acceptance.

Output limits are provider-specific controls, not a quality strategy by themselves. Clear scope and examples often matter more than a very low ceiling.

Evidence & context: Google Cloud

Find tokens hidden in failure loops

Repeated attempts often exceed the savings from compressed wording. Log why a result was rejected: missing context, ambiguous instruction, invalid format, tool error or an unsuitable model. Fix the recurring cause at the workflow level.

Prove that the reduction is safe

1. Record the baseline input, output, pass rate and review time.
2. Change one source of token use.
3. Run the same representative cases.
4. Compare severe failures as well as averages.
5. Keep the change only if the task still meets its acceptance threshold.

Sources & further reading

1. Prompt caching

   OpenAI Developers. Provider-specific guidance on reusing repeated prompt prefixes to reduce latency and input cost. Availability and implementation details vary by model and provider.

2. GenerationConfig REST resource

   Google Cloud. Official documentation showing that output-token limits are a controllable generation setting. Parameter names and behaviour are provider-specific.

3. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

   Lewis and colleagues, 2020. Research combining generation with retrieved material. Retrieval should not be interpreted as proof of factual accuracy.

Examples and exercises are illustrative unless attributed to a source. No independent expert review is claimed.