Prompt engineering course online

Module 1: Introduction to AI Prompt Engineering

1.1 What is Prompt Engineering?

Prompt engineering is the art and science of crafting effective instructions (prompts) for AI models, particularly large language models (LLMs), to elicit the desired output. It involves designing prompts that are clear, specific, and well-structured to guide the model towards generating relevant, accurate, and high-quality responses. The goal is to optimize the model’s performance through carefully constructed prompts.

Here are examples illustrating the concept:

Bad Prompt: “Write a story.”

This is too vague. The AI doesn’t know what kind of story, who the characters are, or what the plot should be.

Good Prompt: “Write a short science fiction story set on Mars, featuring a lone astronaut who discovers an ancient alien artifact. The story should be approximately 500 words and have a suspenseful tone.”

This prompt is much more specific. It provides context (science fiction, Mars), details (lone astronaut, alien artifact), and constraints (word count, tone), giving the AI a much clearer direction.

Bad Prompt: “Summarize this article.”

This is incomplete. The model needs the article itself to summarize.

Good Prompt: “Summarize the following article about the impact of climate change on polar bear populations, focusing on habitat loss and food scarcity: [Insert Article Text Here]”

This prompt provides the model with the source material to summarize, specifies the topic (climate change and polar bears), and indicates key aspects to focus on (habitat loss, food scarcity).

Bad Prompt: “Translate this into French: Hello.”

This is functional, but could be improved.

Good Prompt: “Translate the following English sentence into formal French, as if addressing a government official: Hello.”

This adds context by specifying the tone (formal) and providing a scenario (addressing a government official), influencing the model’s choice of words and grammatical structure.

In essence, prompt engineering is about moving from generic instructions to highly tailored queries that leverage the capabilities of AI models effectively. The better the prompt, the better the output.

1.2 The Importance of Effective Prompts

1.3 Prompt Engineering vs. Traditional Programming

1.4 Applications of Prompt Engineering

Module 2: Fundamentals of Prompt Design

2.1 Prompt Components: Instruction, Context, Input, and Indicator

2.2 Understanding Model Biases and Limitations

2.3 Crafting Clear and Concise Prompts

2.4 Iterative Prompt Refinement

Module 3: Advanced Prompting Techniques

3.1 Few-Shot Learning and Prompt Examples

3.2 Chain-of-Thought Prompting

3.3 Self-Consistency Prompting

3.4 Using Constraints and Guardrails in Prompts

Module 4: Step-by-Step Online Training: Building Prompts for Text Generation

4.1 Generating Creative Content (Stories, Poems, Scripts)

4.2 Summarization and Paraphrasing

4.3 Translation and Language Generation

4.4 Code Generation and Debugging Prompts

Module 5: Step-by-Step Online Training: Prompting for Reasoning and Problem-Solving

5.1 Question Answering Prompts

5.2 Logical Reasoning Prompts

5.3 Mathematical Problem-Solving Prompts

5.4 Planning and Decision-Making Prompts

Module 6: Step-by-Step Online Training: Prompts for Specific AI Models

6.1 Prompting for Large Language Models (LLMs) like GPT-3/4

6.2 Prompting for Open-Source LLMs

6.3 Adapting Prompts to Different Model Architectures

6.4 Understanding Model-Specific Quirks

Module 7: Tools and Platforms for Prompt Engineering

7.1 Prompt Engineering IDEs and Notebooks

7.2 Prompt Libraries and Marketplaces

7.3 API Integration and Deployment

7.4 Collaboration and Prompt Sharing

Module 8: Ethical Considerations and Best Practices

8.1 Bias Detection and Mitigation in Prompts

8.2 Ensuring Responsible AI Use

8.3 Privacy and Data Security in Prompt Engineering

8.4 Avoiding Misinformation and Harmful Outputs