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Use system messages effectively to set context and reduce the need for repeated instructions.

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

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

Don't use a powerful, expensive model like GPT-5 or Claude 4.1 for simple tasks like reformatting text, correcting grammar, or classifying sentiment. Use a smaller, faster, and cheaper model like GPT-4o-mini or a fine-tuned open-source model. This is a key principle of cost-effective AI development.

Mixture of Experts (MoE) is an architecture where instead of one large neural network, the model consists of many smaller 'expert' networks. For any given input, a routing mechanism selects a small subset of these experts to process it. This allows models to scale up their parameter count dramatically while keeping computational costs manageable.

Some tokenizers treat words with leading spaces differently. For example, ' hello' might be a different token than 'hello'. Be consistent with spacing, especially when providing examples, to ensure token efficiency.

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.

If you frequently use long model names in your prompts or code (e.g., 'gpt-3.5-turbo-instruct-0914'), consider using shorter aliases if your framework allows, or use variables to store them. This can slightly reduce token count in complex prompts.

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.

Don't always use the largest, most expensive model. Smaller, faster models can be sufficient and more cost-effective for simpler tasks. Evaluate tradeoffs between capability, speed, and cost.

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.

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.

Challenge: Create the most effective prompt for a complex task using the fewest possible tokens. Test different approaches and measure both output quality and token efficiency.