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Use system prompts to set context for LLMs instead of including context in every message.

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).

Use LLMs to design automated workflows for business processes, including decision trees, approval chains, and exception handling.

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