Craft better prompts for ChatGPT, Claude, Gemini, and more - Prompt like a pro in any AI platforms
InstructorNavid Shirzadi, PhD, P.Eng.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This slide defines iterative prompting as a method to refine inputs over multiple steps, listing applications like code debugging and showing a visual workflow example.
This educational slide explains that large language models predict the next token in a sequence based on training data probabilities, using a bar chart to visualize city name predictions.
This screenshot demonstrates the practical application of prompt engineering skills by showing a real-world interaction with an AI tool to generate multimodal content.
The interface displays a refined prompt describing a futuristic workspace alongside the corresponding AI-generated visual output.
The curriculum categorizes specific methods including Zero-Shot, One-Shot, Few-Shot, Chain-of-Thought, and Role-Based prompting.
Instruction covers how LLMs function through tokenization and provides a conceptual overview of model types.
Some learners found the content focused on fundamental concepts rather than advanced structured formats or complex examples. One signal from June 2026 suggests a need for more depth, though the July 2026 update label does not prove these concerns were addressed.
Instruction relies heavily on ChatGPT, providing limited demonstration of other popular models like Claude. One signal from June 2026 notes this limitation; the July 2026 update label does not guarantee a change in tool variety.
Certain advanced modules, such as those involving coding or Transformers, may exceed the expected skill level for a general audience. One signal from April 2026 indicates these sections can feel disjointed; the July 2026 update label does not prove a correction.
The course targets a general audience, but some technical modules may exceed the skill level implied by the 'for everyone' title.