Background: I developed a rudimentary way to reduce token count for long prompts by concatenating words of a certain length, which has the potential to reduce API token costs by a few % , which can be significant for companies with high API costs from prompt token usage (regardless of their completion token costs which can remain constant).
Question for tokenizing: I am wondering if this approach has any negative affect as output seems unaffected, with completions returning normally.
Background: I developed a rudimentary way to reduce token count for long prompts by concatenating words of a certain length, which has the potential to reduce API token costs by a few % , which can be significant for companies with high API costs from prompt token usage (regardless of their completion token costs which can remain constant).
Question for tokenizing: I am wondering if this approach has any negative affect as output seems unaffected, with completions returning normally.
See this thread with some of the pros/cons: https://community.openai.com/t/removing-spaces-from-prompts-to-maximize-character-limits-i-e-in-gpt-config/684125