Prompt engineering, the process of optimizing prompts for AI models, has been widely used since the introduction of ChatGPT. However, recent research suggests that letting the AI model devise its own prompts yields better results than manual prompt engineering. Autotuned prompts have been found to have successful and strange effects on AI model performance. Companies are increasingly using LLMs for various applications, leading to the evolution of the job role of prompt engineers and the emergence of LLMOps. Deploying AI products involves challenges such as reliability, adapting outputs, testing, safety, and compliance. Prompt engineering will continue to evolve and change as AI models and the job landscape develop.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Increase in AI prompt engineering | Shift from human-engineered to AI-engineered prompts | AI models optimizing prompts themselves | Desire for more effective and efficient prompts |
| Questioning the future of prompt engineering | Potential decline in prompt-engineering jobs | Possibility of prompt engineering being automated | Research showing the effectiveness of AI-generated prompts |
| Autotuned prompts are successful and strange | Inconsistent results in prompt engineering | AI models generating more optimal and unique prompts | The nature of algorithms and their ability to optimize |
| Automation of prompt engineering | Transition from manual trial and error to automated prompt generation | Faster and more efficient prompt optimization | Desire for improved prompt engineering process |
| Application of autotuned prompts in image generation | Better image generation through automated prompt optimization | Enhanced quality of generated images | Need for improved prompt engineering in image generation |
| Prompt engineering jobs will still exist | Evolution and adaptation of prompt engineering jobs | Continued need for human involvement in AI model development | Complexity and challenges in deploying AI models |
| Emergence of large language model operations (LLMOps) | Expansion of job roles in language model deployment | Integration of prompt engineering into broader range of tasks | Necessity for comprehensive AI model deployment processes |
| Uncertainty and rapid change in the field of prompt engineering | Lack of established rules and evolving nature of prompt engineering | Continuous evolution and transformation of prompt engineering practices | Emerging field with changing landscape and dynamics |