Pre-training and the emergence of transformers
The introduction of transformers, specifically with the “Attention Is All You Need” paper by Vaswani et al. in 2017, the field of NLP. Transformers made it possible to pre-train language models on a broad scale and teach them how to represent words and sentences in context. However, throughout this time, prompt engineering was still a relatively unexplored technique.A major turning point for rapid engineering occurred with the introduction of OpenAI’s GPT models. GPT models demonstrated the
and fine-tuning on particular downstream tasks. For a variety of purposes, researchers and practitioners have started using quick engineering techniques to direct the behavior and output of GPT models.As the understanding of prompt engineering grew, researchers began experimenting with different approaches and strategies. This included designing context-rich prompts, using rule-based templates, incorporating system or user instructions, and exploring techniques like prefix tuning.
Prompt engineering continues to be an active area of research and development. Researchers are exploring ways to make prompt engineering more effective, interpretable and user-friendly. Techniques like rule-based rewards, reward models and human-in-the-loop approaches are being investigated to refine prompt engineering strategies.Prompt engineering is essential for improving the usability and interpretability of AI systems.