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Consider using shorter models for simple tasks to save on API costs while maintaining quality.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

Many LLM APIs allow you to specify which fields you want in the response. Only request the data you need (e.g., just the generated text, not full logprobs if unused) to reduce data transfer and processing.

If you have many small, independent tasks for an LLM, batch them into a single API call if the model and API support it, rather than many separate calls. This can reduce overhead.

LLMs are excellent at generating boilerplate code for common patterns (e.g., setting up a new class, a basic API endpoint, HTML structure).

Beyond exact-match caching, consider semantic caching. If a new prompt is semantically similar to a previously cached one, you might be able to reuse the old response, potentially saving an API call.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.