Generative AI 101

How do you evaluate Generative AI?

In this episode of Generative AI 101, we explore evaluating Generative AI large language models (LLMs). Just like finding the best restaurant in town means more than judging a single dish, evaluating AI models requires a comprehensive approach. We break down why assessing performance, comparing models, and building user trust are central to evaluation. From authenticity to speed, and fairness to cost, we cover the key factors that determine an AI's true potential. 
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of generative AI. And remember, you now know more about LLM Evaluation than you did before!
 
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What are AI Hallucinations?

In this episode of Generative AI 101, we’re tackling the curious case of AI hallucinations—when AI creates content that’s completely off the mark. We’ll explore how these digital daydreams happen, why humans aren’t immune to similar slip-ups, and the impact these hallucinations can have. Whether amusing or alarming, AI hallucinations are a phenomenon you’ll want to understand.
 
Check out this Paper: WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
 
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of generative AI. And remember, you didn't hallucinate this episode, no, this actually happened.
 
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Fine-Tuning GenAI Prompts

In this episode of Generative AI 101, we’re fine-tuning your AI prompting skills. Learn how to refine prompts to turn mediocre responses into top-tier results. We’ll cover techniques like iterative prompting, using context, and applying constraints, along with ethical considerations to ensure fairness. Get ready to make your AI work smarter, not harder.
 
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of generative AI. And remember, you now know more about fine-tuning your AI prompts than you did before!
 
Connect with Emily Laird on LinkedIn

The Bias & Blunders of GenAI

In this episode of Generative AI 101, we’re exploring the various flavors of generative AI bias and inaccuracy—those pesky issues that make your AI sound like it’s stuck in a 1950s sitcom. From data-driven biases baked into AI’s training to sneaky algorithmic quirks and the all-too-human flaws we project onto our creations, we explore how to recognize and mitigate these pitfalls. Learn how strategic prompting can tone down clichés and overgeneralizations, making your AI smarter and fairer. Plus, we’ll share tips on using tools like IBM’s AI Fairness 360 to keep your AI from going rogue.
 
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of generative AI. And remember, you now know more about generative AI bias and inaccuracy than you did before!
 
Connect with Emily Laird on LinkedIn

What is BAB Prompting?

In this episode of Generative AI 101, we catch a wave into the BAB framework (we're totally calling it "BABE")—an effortlessly cool method for crafting AI prompts that turn "meh" into "tubular!" BAB, short for Before, After, and Bridge, guides you from dull, lifeless AI interactions to responses that shine like a pro surfer catching the perfect wave. Learn how to structure your prompts for clarity, precision, and maximum impact, ensuring your AI delivers every time. 
If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of AI and prompt engineering. And remember, you now know more about BAB prompting than you did before!Connect with Emily Laird on LinkedIn

What is CARE Prompting?

In this episode of Generative AI 101, we explore CARE Prompting—a sophisticated method that prioritizes Craft, Audience, Response, and Evaluation to fine-tune AI outputs. CARE Prompting is designed to create responses that go beyond accuracy, ensuring they resonate deeply and reflect a comprehensive understanding of your goals.If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of AI and prompt engineering. And remember, you now know more about chain-of-thought prompting than you did before!Connect with Emily Laird on LinkedIn

What is RACE Prompting?

In this episode of Generative AI 101, we’re taking a high-speed tour of the RACE Prompting method—where Role, Audience, Context, and Example aren’t just pit stops, but the keys to better communication. 
Ready to take your AI skills to the next lap? Connect with Emily Laird on LinkedIn. Stay tuned for more laps around the ever-evolving circuit of AI and prompt engineering. Connect with Emily Laird on LinkedIn

What is RTF Prompting?

In this episode of Generative AI 101, we explore the ins and outs of RTF Prompting—an approach that sharpens your AI's focus by defining its Role, Task, and Format. Whether you're tasking your AI with writing a travel blog, analyzing financial data, or extracting historical records, RTF Prompting provides the clarity and structure needed to achieve precise results. We’ll break down how to use this versatile tool, discuss its pros and cons, and share some entertaining examples that highlight its effectiveness.
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of AI and prompt engineering. And remember, you now know more about RTF Prompting than you did before!
Connect with Emily Laird on LinkedIn.

What is Self-Consistency Prompting?

In this episode of Generative AI 101, we break down the concept of self-consistency prompting—a technique that enhances AI accuracy by posing the same question in multiple ways and selecting the most consistent answer. By guiding your large language model through varied prompts, this method improves performance on complex reasoning tasks and reduces errors. However, it's not without challenges: it demands more computational power and isn't entirely foolproof.Self-Consistency Prompting paper
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of AI and prompt engineering. And remember, you now know more about self-consistency prompting than you did before!
Connect with Emily Laird on LinkedIn

What is Chain-of-Thought (CoT) Prompting?

In this episode of Generative AI 101, we break down the concept of chain-of-thought prompting—a technique that helps AI models think through complex tasks step by step, improving their logical and accurate responses. By guiding the AI, or your large language model of choice, through detailed instructions, this method enhances performance on tasks like arithmetic and decision-making. However, it’s not without challenges: it works best with large models and can be resource-intensive.
Connect with Us: If you enjoyed this episode or have questions, reach out to Emily Laird on LinkedIn. Stay tuned for more insights into the evolving world of AI and prompt engineering. And remember, you now know more about chain-of-thought prompting than you did before!Connect with Emily Laird on LinkedIn