
Try this research prompt framework to get sharper, more useful analysis from AI
strAIght talk: AI tips for AmazoniansShow Notes
It took 20 iterations. Now, the prompt framework a program manager from Amazon's Global Corporate Communications team built consistently delivers high-quality research analysis — faster and more precisely than before. Listen to find out how she got there — and how you can apply the same approach to your own research process. Guest: Caitlin Gallagher, Program Manager Note: Some tools, resources, and internal references discussed in this podcast may only be accessible to Amazon employees. Disclaimer: The views, techniques, and tool preferences shared in this podcast represent the personal experiences and opinions of the host and guests. Always follow your organization’s security and compliance guidelines when implementing AI solutions.
More from strAIght talk: AI tips for Amazonians
Alexa for Shopping combines product expertise with personal context to help customers find, compare, and buy what they need. A Senior Manager on the team explains the AI behind the experience and where it's headed next.
What if your documents got VP-level feedback before the actual review? On this episode of strAIght talk, hear how one builder turned a decade of leadership feedback into an AI-powered review tool now used by over a thousand colleagues each week.
Beth Galetti, SVP of People Experience and Technology, hadn't written code in 25 years. Then she built a live website with AI on a transatlantic flight and challenged every person in her organization to build something in two hours or less. Over 1,000 did.
The Working Backwards AI tool was built to be your toughest thinking partner for product ideation. The tool walks you through five customer questions, generates personas who react to your idea, and brings in domain experts to test your thinking. Over half of users say their final idea was different from what they started with.
Telling your team to "experiment with AI" without a structure usually leads to scattered pilots that fizzle out. One leader built a model that loads both ends: a dedicated Tiger team reimagining one high-value workflow while the broader org gets protected time to skill up and share what's working. On this episode, our guest walks through the full framework, how the two sides feed each other, and why getting to a disappointment fast is more valuable than waiting months for a polished demo. His biggest tip: pick one workflow where the pain is obvious, give a single owner two weeks, and ship something working.