For a career in prompt engineering, one should focus on acquiring knowledge and skills in these sectors mainly:

1. Natural Language Processing (NLP): Understanding the fundamentals of NLP, including text processing, sentiment analysis, and language modeling.
2. Machine Learning (ML): Grasping core ML concepts, algorithms, and frameworks, particularly those relevant to NLP, such as supervised and unsupervised learning.
3. Deep Learning: Familiarity with neural networks, specifically architectures like Transformers, which are crucial for advanced language models.
4. Programming Languages: Proficiency in Python is essential, given its extensive libraries and frameworks for NLP and ML, like TensorFlow, PyTorch, and Hugging Face’s Transformers.
5. Data Preprocessing: Skills in cleaning, tokenizing, and vectorizing text data to prepare it for modeling.
6. Prompt Design: Techniques for crafting effective prompts to elicit desired responses from language models, including understanding prompt tokens, embeddings, and context setting.
7. API Usage: Experience with APIs of popular language models, such as OpenAI’s GPT, to integrate and deploy models in applications.

These technologies and skills form the foundation for a successful career in prompt engineering, enabling one to design, implement, and optimize prompts effectively.