Generative AI and Prompt Engineering for Founders

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About Course

Generative AI can help a founder research faster, communicate better and automate repetitive work—but impressive output is not the same as reliable business value. Many founders collect prompts and subscribe to tools without knowing how to frame tasks, verify answers, protect confidential information or turn isolated experiments into repeatable workflows. This course gives you a practical system for using generative AI with greater control and judgment. You will learn how generative AI works, compare models and tools, and master a prompt framework that transfers across platforms. You will practise iterative prompting with documents and multimodal inputs, then apply it to research, analysis, decisions, marketing, sales, fundraising and operations. The course moves beyond clever prompts into reusable templates, task-specific assistants, connected workflows and responsible controls. Every section produces something usable, including a tool-selection scorecard, prompt library, verification checklist, AI workflow and founder AI-use policy. By the end, you will become an AI-enabled founder: able to design effective prompts, judge output quality, build repeatable AI systems and decide where human oversight remains essential. You will leave with a 90-day AI adoption roadmap grounded in measurable value, cost and risk. Successful learners receive a Scalelab Academy certificate, lifetime course access, future content updates and access to course-specific AI tools for generating, testing and improving prompts, workflows, policies and business use cases.

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What Will You Learn?

  • Explain generative AI capabilities and limitations in practical terms.
  • Select suitable AI models and tools for specific business tasks.
  • Write structured prompts with context, constraints and output criteria.
  • Improve results through decomposition, examples and iterative critique.
  • Use AI for research and analysis while verifying important claims.
  • Create audience-specific content while preserving brand voice.
  • Turn effective prompts into reusable templates and assistants.
  • Design AI-enabled workflows with human review and exception handling.
  • Protect confidential data, intellectual property and customer interests.
  • Establish practical responsible-AI rules for a startup team.
  • Evaluate AI use cases using value, feasibility, evidence and risk.
  • Build and present a measurable 90-day AI adoption roadmap.

Course Content

Section 1: Understand Generative AI without the Hype
Build a clear founder-level understanding of what generative AI does, how it produces outputs and why confident answers may still be wrong. Learners will distinguish models, applications and interfaces, explore common capabilities and limitations, and select realistic expectations for business use.

  • Distinguish artificial intelligence, machine learning and generative AI
  • Understand models, tokens, context and generated outputs
  • Explore text, image, audio, video and multimodal capabilities
  • Recognise hallucination, bias and knowledge limitations
  • Separate fluent output from verified evidence
  • Map realistic generative AI opportunities for a venture

Section 2: Choose the Right AI Models and Tools
Select AI tools according to the task rather than popularity. Learners will compare general assistants, reasoning and research tools, creative generators, coding support and specialised applications using capability, cost, speed, privacy, integration and reliability criteria.

Section 3: Master the Foundations of Prompt Engineering
Use a practical prompting framework that transfers across AI platforms. Learners will define the task, supply relevant context, specify constraints, assign useful perspectives, demonstrate the desired output and request structured responses that are easier to review.

Section 4: Improve Prompts through Iteration and Context
Move beyond one-shot prompting to structured conversations that improve results. Learners will practise decomposition, follow-up instructions, examples, critique-and-revision loops, file-based and multimodal prompting, and disciplined context management.

Section 5: Use AI for Research, Analysis and Decisions
Apply generative AI without allowing it to replace evidence or founder judgment. Learners will frame research questions, explore markets, analyse documents, compare options and test assumptions while verifying sources, calculations and consequential claims.

Section 6: Create Better Business Content and Communication
Use prompts to produce useful first drafts for marketing, sales, fundraising, customer support and internal operations. Learners will define audiences, control tone, preserve brand voice, repurpose source material and review outputs for accuracy, originality and commercial usefulness.

Section 7: Build Reusable Prompt Systems and AI Assistants
Turn successful prompts into repeatable business assets rather than isolated chat history. Learners will create templates, variables, instruction hierarchies, knowledge collections, quality checks and task-specific assistants that team members can use consistently.

Section 8: Design AI Workflows, Automation and Agents
Move from individual conversations to connected business workflows. Learners will identify suitable processes, combine AI with business tools, understand APIs and automation at a founder level, introduce human approval points and distinguish dependable workflows from premature autonomous agents.

Section 9: Manage AI Risk, Privacy and Responsible Use
Protect customers, employees and the venture while adopting generative AI. Learners will address confidential information, personal data, intellectual property, bias, fabrication, harmful content, cybersecurity, vendor risk and accountability, then create a practical founder AI-use policy.

Section 10: Build the Venture’s 90-Day AI Action Plan
Convert experimentation into disciplined adoption. Learners will prioritise use cases by value, feasibility and risk; establish baselines; estimate costs and benefits; define evaluation criteria; assign owners; and create a realistic roadmap for responsible implementation.

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