TRIMLAYER LEARN · EVIDENCE-AWARE CONTEXT
How to reduce OpenAI API input costs
OpenAI input cost depends on the tokens your application sends. Long RAG results, conversation history and tool output can often be narrowed before your direct model call.
Practical guidance
Measure the input boundary
Track original input, optimized input and any recovery input. Separate input savings from output-token cost, tool charges and cached-input pricing so the estimate remains auditable.
Practical guidance
Optimize before the model call
Send source context and the current task to TrimLayer, receive optimized_context, then place that result in your own OpenAI request. TrimLayer does not receive your OpenAI key or call the model.
Practical guidance
Protect cache strategy
Avoid changing stable prefixes without a measured reason. Optimize the variable retrieved context or history segment and compare the resulting provider cache behavior on your workload.
Practical guidance
Use the right workload
Detailed contracts, support histories and multi-document RAG often contain removable context. Very short prompts or broad whole-document summaries may yield little or no reduction.
Practical guidance
Validate the net result
Compare provider input tokens, answer equivalence, critical-evidence recall, fallback rate, recovery and p95 latency. Savings should be reported after recovery and optimization overhead.