Token Optimization Methods to Reduce Artificial Intelligence Costs
A user investigated the cause of high costs on an AI dashboard and shared practical optimization and savings methods to reduce token consumption.
Realizing that expenditures on the AI dashboard were quietly increasing, a user reported achieving significant savings by implementing various optimization strategies, ranging from prompt structure to model selection, to lower token costs.
The Cause of Rising Artificial Intelligence Costs
Investigating the reason for increasing expenditures on the AI dashboard, the user realized that token consumption lay at the root of the issue. It became clear that every command sent and response received was billed as small text chunks, and long prompts filled with polite expressions increased the cost.
Input and Output Token Management
When it was observed that output tokens are much more expensive than input tokens, long responses were restricted. Polite words in the commands were removed, bullet points were used instead of paragraphs, and output limits were clearly defined, achieving a rapid drop in costs.
Chat History and Document Context
The issue of resending the entire chat history with every call was resolved by summarizing past responses every ten turns. Instead of pasting entire documents, a system was established where the most relevant parts were retrieved using embedding models.
Model Selection and Caching
Abandoning the use of the largest and most capable model for every operation, simple tasks were directed to smaller models. Significant discounts in costs were achieved through prompt caching, a method where static contents are placed at the beginning of commands.
Savings Results Achieved
Thanks to these optimizations and monthly audits, a drastic reduction was achieved in the cost of the same workload. This process highlighted the reality that every word has a cost in daily artificial intelligence usage.