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Prompt Engineering for Business

Master the art of communicating with AI models to get better business results. Learn proven techniques from basic prompting to advanced methods like chain-of-thought reasoning and few-shot learning.

20 lessons in 5 modules

Course curriculum

1

Foundations of Prompt Engineering

Learn the fundamentals of effective AI communication and prompt structure

4 lessons

Understanding AI Language Models

15m
Free preview

{"body": "AI language models like GPT process text by predicting the most likely next words based on patterns learned from vast datasets. Understanding this helps you craft better prompts. Models don't truly 'understand' but excel at pattern matching and completion.

Key principles: AI responds to context, specificity improves results, and models can exhibit different 'personalities' based on how you frame requests. Think of prompting as giving detailed instructions to a highly capable but literal assistant.

Practical example: Instead of 'Write about marketing,' try 'Write a 200-word summary of digital marketing trends for B2B software companies in 2024, focusing on lead generation strategies.' The specific context, length, and focus dramatically improve output quality.

Exercise: Take a vague request you might typically make to AI and rewrite it with specific context, desired format, and clear objectives. Notice how the additional detail guides the AI toward your intended outcome.", "type": "text"}

Anatomy of an Effective Prompt

20m
Free preview

{"body": "Effective prompts have four key components: Context (background information), Task (what you want done), Format (how you want the output), and Tone (the style or voice). This CTFT framework ensures comprehensive communication with AI.

Context sets the stage: 'You are a sales manager at a SaaS company.' Task defines the objective: 'Create a follow-up email sequence.' Format specifies structure: 'Write 3 emails, 150 words each.' Tone guides style: 'Professional but friendly tone.'

Order matters: Start with context, then task, followed by format requirements, and end with tone instructions. This logical flow helps AI maintain focus throughout its response.

Practical example: 'Context: You're analyzing Q3 sales data for a retail company. Task: Identify the top 3 performance trends. Format: Bullet points with supporting metrics. Tone: Executive summary style for board presentation.' This structure produces focused, actionable insights.

Exercise: Apply the CTFT framework to a recent AI request you made. Rebuild it using all four components and compare the results.", "type": "text"}

Common Prompting Mistakes

18m
Members only

Setting Clear Objectives

17m
Members only
2

Advanced Prompting Techniques

Master sophisticated methods like chain-of-thought reasoning and few-shot learning

4 lessons

Chain-of-Thought Prompting

22m
Members only

Few-Shot Learning Examples

25m
Members only

Role-Based Prompting

20m
Members only

Constraint-Based Prompting

23m
Members only
3

System Prompts and Templates

Create consistent AI behavior patterns and reusable prompt frameworks

4 lessons

Creating Effective System Prompts

25m
Members only

Building Prompt Templates

28m
Members only

Consistency Across Conversations

24m
Members only

Version Control for Prompts

26m
Members only
4

Business-Specific Prompt Libraries

Develop specialized prompts for writing, analysis, coding, and automation tasks

4 lessons

Writing and Communication Prompts

27m
Members only

Analysis and Research Prompts

29m
Members only

Process Documentation Prompts

26m
Members only

Automation and Workflow Prompts

30m
Members only
5

Optimization and Troubleshooting

Refine prompts for better results, measure effectiveness, and solve common problems

4 lessons

Measuring Prompt Effectiveness

25m
Members only

Iterative Prompt Refinement

28m
Members only

Common Issues and Solutions

24m
Members only

Advanced Optimization Strategies

32m
Members only
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