The GenAI model learns tasks from examples in the prompt
Image: Liz, CC BY 2.0, via Wikimedia Commons
Prompt engineering
The GenAI model learns tasks from examples in the prompt
Prompt engineering involves structuring natural language inputs to guide GenAI models in generating desired outputs. This process includes designing and refining input instructions to improve accuracy and relevance.
Example
Few-shot prompting involves providing a GenAI model with a few examples to learn and perform a task, such as summarizing a text.
Understanding prompt engineering is crucial for effectively utilizing GenAI models in various applications.
Related concepts
Retrieval-augmented generation
RAG enables LLMs to access new information without retraining
Knowledge distillation
Knowledge distillation transfers knowledge from a large model to a smaller one without loss of validity
mean pooling often outperforms [CLS] for sentence similarity tasks
Mean pooling captures overall sentence meaning better than [CLS] token embedding
weight initialization matters: Xavier/He init keeps activation variance ≈ 1 across layers
Weight initialization stabilizes learning by maintaining consistent activation variance
Adam combines momentum and RMSprop: adapts per-parameter learning rates
Adam combines momentum and RMSprop by adapting per-parameter learning rates
[CLS] pooling does: uses the first token's embedding as the sentence representation
CLS pooling: uses the first token's embedding as the sentence representation
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