Optimizing your LLM prompts is essential for reducing cost, improving latency, and enhancing response quality. While many applications work with basic prompts, there’s often significant room for improvement with minimal effort. Here are four effective techniques to help you get the most out of your LLMs. First, always place static content at the beginning of your prompt. Static content—such as system instructions, role definitions, or fixed rules—remains consistent across requests. Major LLM providers like OpenAI, Anthropic, and Google use cached tokens, which are processed more quickly and cheaply when they’ve been seen before. Input tokens that are cached typically cost around 10% of normal input tokens. To benefit from this, structure your prompt so that the static parts come first, followed by variable content like user questions or document text. For example: prompt = f"""{static system instructions}{document content}{user question}""" If you're processing the same document multiple times, keep the document content in the static section to ensure it’s cached. However, note that caching usually only applies if the first 1024 tokens are identical across requests. Avoid placing variable content at the start, as this breaks the cache and increases cost and latency. Second, place the user question at the end of the prompt. This simple change can improve performance by up to 30%, especially in long-context scenarios. When the question comes last, the model better understands the task it’s being asked to perform. A clear structure like this works well: system_prompt = "You are a helpful assistant. Always respond in JSON format." user_prompt = "What is the capital of France?" This format helps the model focus on the specific request and leads to more accurate, consistent responses. Third, use a prompt optimizer. Human-written prompts often include redundancy, poor structure, or unclear instructions. Feeding your prompt into another LLM to improve it can quickly generate a cleaner, more effective version. For even better results, use built-in prompt optimizers available in platforms like OpenAI’s and Anthropic’s dashboards. These are specifically designed to refine prompts for clarity, conciseness, and task alignment. Adding context such as expected output format, desired tone, or key constraints can further boost results. This process usually takes just 10 to 15 minutes and can yield major improvements. Fourth, establish your own LLM benchmarks. Not all models perform equally on every task. Test different models—such as OpenAI’s GPT series, Anthropic’s Claude, and Google’s Gemini—on your specific use case. Set up a consistent evaluation framework to measure accuracy, speed, and cost. Regularly re-evaluate models, as providers frequently update their models without changing the version name. You should also consider testing open-source models, though they may require more setup and infrastructure. In summary, by using cached tokens, placing the user question at the end, applying prompt optimizers, and running custom benchmarks, you can significantly improve your LLM application’s efficiency and output quality. These techniques are easy to implement and deliver high returns with little effort. Always stay curious and keep testing new methods—small changes can make a big difference.