People often claim prompt engineering is dead with each new model release, but it continues to be relevant and effective.

The future will likely see more researchers embedded in every product team, as fine-tuning models for specific use cases becomes a core workflow for building products.

A good prompt engineer should be able to step back from their own knowledge and communicate the necessary information clearly to the model.

Prompt engineering is about getting the model to perform tasks effectively by clear communication and understanding the model's psychology.

Developers should be agile and continuously experiment with technology, as many ideas may not work initially but could succeed in future iterations of the models.

The better the AI models become, the more opportunities there are for startups to leverage these advancements. Startups need to be proactive in building their products in anticipation of new model releases rather than reacting to them.

As AI and large language models evolve, startups may need to hire more software engineers who understand these technologies to automate processes and drive growth post-product market fit.

If your product's success depends solely on the models getting smarter, you're likely building the wrong thing. Instead, create something that improves as the models advance.

The skill of prompting AI models is crucial; those who understand how to communicate effectively with these models can create more sophisticated and creative outputs.