Prompt Engineering

What is Prompt Engineering?

🔧 Developing, designing, and optimizing prompts to enhance the output of FMs for your needs
- Simple prompts don't give enough guidance and leave a lot up to model's interpretation
Components of enhanced prompting technique:
- Instructions: a task for the model to do (description, how the model should perform)
- Context: external information to guide the model
- Input data: the input for which you want a response
- Output Indicator: the output type or format
Negative prompting technique: explicitly instruct the model on what not to include or do in its response
- Helps to avoid unwanted content, maintain focus, enhance clarity
Simple prompting, enhanced prompting and negative prompting examples:

LLMs generate text based on word probability in a sentence
- Selection is random but weighted
- Example
Certain prompt/inference parameters can be tweaked in an LLM to change weighting, e.g. making the LLM more or less creative
Screenshot of Prompt Configurations and Inference Parameters in Amazon Bedrock

System Prompts: how the model should behave and reply
Temperature (0 to 1): creativity of the model’s output
- Low (ex: 0.2) → outputs more conservative, repetitive, focused on most likely response
- High (ex: 1.0) → outputs more diverse, creative, and unpredictable, maybe less coherent. Higher chance of hallucinations.
Top P (0 to 1): top percentile of probable words to consider
- Low P (ex: 0.25) → consider the 25% most likely words, will make a more coherent response
- High P (ex: 0.99) → consider a broad range of possible words, possibly more creative and diverse output
Top K: limits the number of probable words
- Low K (ex: 10) → only top 10 probable words considered, more coherent response
- High K (ex: 500) → top 500 probable words considered, more words, more diverse and creative response