A case study from Loop Earplugs shows how an AI assistant named Martha doubled positive response rates from 32% to 60%.
Companies often fail to define financial KPIs for AI initiatives, but measuring metrics like error reduction and satisfaction scores is crucial, as shown by Klarna's experience.
To get consistent results from an LLM, you need a paid subscription, a specific and repeatable task to automate, a structured prompting framework, and patience for iteration.
The RACE framework (Role, Act, Context, Execute) provides a structure for creating detailed prompts that make implicit requirements explicit for more consistent AI outputs.
A custom GPT named 'Martha 2.0' demonstrates how to generate personalized, multi-sequence outreach messages by processing a LinkedIn profile and job description.
A comparison of ChatGPT, Gemini, and Claude for an outreach task shows varied results, highlighting that a reliable custom GPT is the foundation for building advanced AI agents.
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Ismaning, Germany
26:04José Kadlec
31:39Rainer Stropek
20:47Data Dan
25:03Alla Pavlova
26:54Duan Lightfoot
30:24Markus Walker
25:17Scott Hanselman
21:43Guy Barel
Jobs that call for the skills explored in this talk.
Agile Talent
Collaboration Betters The World GmbH
OpenAI Inc.
San Francisco, United States of America
Promptora AI Solutions C-corp
San Francisco, United States of America
