2 Understanding Transformers
This chapter covers
- Leveraging knowledge of transformer architecture components like attention and positional encoding to design effective prompts
- Handling input constraints such as long inputs using truncation, summarization, and segmentation techniques
- Guiding model attention with clear context, and natural language patterns to improve output quality
- Controlling output generation by tuning parameters like temperature and sampling methods for diversity or precision
- Aligning prompts with model pre-training and fine-tuning, providing context for tasks beyond the model's knowledge cutoff
- Iterating and refining prompts through experimentation and evaluation to optimize performance
Let's dive into the technical foundations of transformer-based language models and explore how they relate to prompt engineering. In the previous chapter, we discussed the impact of these models on the field of natural language processing and the role prompt engineering plays in getting optimal performance out of these tools. To create high-quality prompts that effectively leverage the capabilities of transformers, it's beneficial for prompt engineers to grasp the underlying technical concepts.