In this episode of Answer Engines: How Brands Build the AI Advantage by Advantage Labs, we explore everyday AI tools. Learn how to write better AI prompts with a simple 4-part framework—role, context, task, format—plus common mistakes to avoid, iteration tips, and real examples.
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Learn how to write better AI prompts with a simple 4-part framework (role, context, task, format) to boost accuracy and reduce editing. We cover prompt engineering essentials, ChatGPT prompting tips, common mistakes to avoid, and live iteration examples you can copy today.
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This episode covers:
What is the best structure for an AI prompt?
Use a 4-part framework: set the role or perspective, add clear context and goals, specify the task, and define the output format. Include audience and tone when relevant.
What common prompt engineering mistakes should I avoid?
Avoid being vague, asking for too much at once, leaving out audience or tone, skipping examples, failing to state what not to do, and not iterating on the response.
How do I iterate when AI output misses the mark?
Give precise revision instructions, specify the exact output format (e.g., "output: list of URLs"), add constraints or examples, and ask the model what details it still needs.
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About the Show
Answer Engines: How Brands Build the AI Advantage by Advantage Labs is a podcast by Advantage Labs exploring how brands earn visibility, trust, and recommendations in the age of AI-driven search. Through conversations with business leaders, founders, and technologists, the show examines how artificial intelligence is reshaping discovery, authority, and decision-making across systems like ChatGPT, Google's AI-powered search experiences, and other emerging answer engines.
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Host:
Sheridan Wendt
Visionary technology leader with 15+ years of experience driving transformative change, delivering $4B+ in global infrastructure projects, and leading AI implementations that improve efficiency and revenue across Fortune 500 organizations.
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Guest:
Jonathan Foster
A seasoned Cyber Security Engineer/Analyst with a diverse background in network systems and electronics. Over his career, Jonathan has worked with KBR, Inc., VT Group, Serco, and the US Navy. His expertise includes leading system installations, technical support, and network security for military-grade operations. Notably, he has managed shipboard network systems, ensured compliance with military standards, and trained personnel on system maintenance and troubleshooting. Jonathan's experience also spans handling classified material, optimizing network uptime, and auditing security policies.
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Chapters
00:00 – Introduction
00:30 – Welcome & why prompt engineering
01:13 – Why prompting matters
02:36 – What is a prompt?
04:41 – The 4-part prompt framework
05:11 – Real estate plan prompt upgrade
05:22 – Bad prompt: Where can I buy a shirt?
06:46 – Refining role and context
08:18 – Combining task and format
08:48 – Common prompting mistakes
12:09 – Specify outputs with output:
12:53 – Using colons and parentheses
14:54 – Clear constraints example
18:49 – Live example: dress URLs
19:12 – Iterate to email format
20:23 – Templates vs DIY prompts
20:56 – Model settings & clarifying Qs
21:56 – Use one model to prompt another
22:39 – Can AI test its own work?
25:27 – Prompt use cases & starters
26:42 – Power of iteration
27:19 – Ask what info is missing
28:14 – Key takeaways & challenge
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Learn more about Advantage Labs:
https://advantagelabs.ai
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Watch on YouTube:
https://www.youtube.com/@AdvantageLabsAI
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Listen on more platforms:
https://podcast.AdvantageLabs.ai
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