Advanced Prompt Concepts
Learning Outcomes
Get More Info
data, Tech & ai skills in
What’s Included
Course Introduction
Course Introduction
In this lesson, we’ll map out the steps of the interaction process between prompts and language models. This process has an aim to generate and optimize the prompt output.
Processing Promts
Processing Promts
In this lesson, we’ll dive deeper and talk about embeddings that handle prompt context to convert words, phrases, or other types of data into a language that machines can understand.
Enhancing Model Understanding
Enhancing Model Understanding
Transformers are the second piece in a puzzle, along with embeddings, whose understanding makes us more cautious when writing prompts. Transformers help machines interpret the data, giving the green light to the model to proceed with the output generation.
Guiding Model Performance
Guiding Model Performance
In this lesson we’ll learn how model optimization techniques, such as fine-tuning and prompt tuning, enable language models to respond to task-specific prompts.
Understanding Token Limits and Temperature
Understanding Token Limits and Temperature
Control parameters, once adjusted, affect generated texts. In this lesson we’ll see how adjusting the token limit affects text quality and how adjusting temperature has an effect on randomness or creativity of the text generated by a model.
Controlling Output with Other Parameters
Controlling Output with Other Parameters
It is of no less importance to mention other parameters that control the output of the prompt. In this lesson, we’ll learn about stop sequence, top_p and top_ k LLM control parameters that affect the length, diversity and the selection of the next word in a sequence, respectively.
Certifications
A Learner Experience Designed to Deliver Transformation
How Leading Teams Use Kubicle to Upskill & Excel
