Reading prompts and model outputs closely helps improve prompting skills; experimentation is key.

When developing a new large language model product, it's essential to iterate on prompts to find what works best for your specific use case.

The best prompting can significantly impact the success of an experiment, sometimes making the difference between marginal and substantial model performance.

As models improve, it's often more effective to communicate the specific task directly rather than using metaphors or role-based prompts.

Including additional information or context in prompts significantly improves the model's understanding and output quality.

Chain of thought prompting, where you instruct the model to think step-by-step, is less necessary with modern reasoning models but can still be useful in some cases. If you're running thousands or millions of inputs through your prompt, you'll still need to use classical prompting techniques to make your prompt more robust.

You can enhance your prompting by including examples in your prompt. For instance, provide a problem and a good answer, and then ask the model to solve a similar problem.

The future of prompting may involve more guided interactions with the model, rather than just typing into a text box.

Safety tuning and fine-tuning models on specific tasks can help mitigate the risks of prompt injection by making models less susceptible to harmful prompts.