Need answers you can trust, with sources to back them up? In this episode, we dive into Perplexity AI, the "answer engine" that blends a search engine with conversational AI. And find out how they're changing the internet with their new browser, Comet!

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This week we're covering OpenAI's current lineup of models available in ChatGPT, and comparing them to Google's Gemini and Anthropic's Claude models.

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OpenAI Model Comparison

Google gave straight-forward names with the 3 Gemini models. Now lets dive into Anthropic's trio of creatively named models and how they compare.

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Anthropic Model Comparison

Depending on who you talk to, you’ll either hear that AI is great and so helpful, or is horrible and doesn’t give good answers. Some of this may be due to prompting techniques or intended use cases, but it may also be due to the wrong model being used. Each of the major model providers has multiple models to choose from. In this episode, we’ll cover Google’s Gemini Models, giving you an overview of the major models, when to use each, pros and cons, and some additional Gemini tools.

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https://deepmind.google/models/gemini/

Before you dive into picking the “best” AI model for the job, you’ve got to understand what kind of model you’re dealing with. In this episode, we break down three essential distinctions: chat vs. API, hosted vs. local, and proprietary vs. open-source.

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In this episode of the 8minute.ai Podcast, we discuss prompting techniques such as shot-based, chain-of-thought, chaining, and iterative to help you refine your requests like a pro!

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What’s the secret to getting AI to do exactly what you want? It’s all in the prompt. In this episode, Garvin, Becka, and Michael break down the basics of prompt engineering—how to ask smarter questions, add context, and guide tone and format to get better results from tools like ChatGPT and Gemini. Whether you're planning a trip, writing code, or just craving a better dinner idea, this 8-minute crash course will help you talk to AI like a pro.

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In this episode of 8minuteAI, Becka, Michael, and Garvin break down the magic behind Large Language Models (LLMs), from how LLMs predict the next word to why they sometimes get weird (mayonnaise in a PB&J sandwich, anyone?), we explore how these models work, why they’re powerful, and what makes them occasionally... hallucinate. Tune in to learn how LLMs really “think”, without needing a computer science degree.

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Mentioned in the show: OpenAI Tokenizer