Almost Timely News: A Guided Tour of the Most Powerful Generative AI Model Google Offers (2024-03-24)
Almost Timely News: A Guided Tour of the Most Powerful Generative AI Model Google Offers (2024-03-24) :: View in Browser
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What's On My Mind: A Guided Tour of the Most Powerful Generative AI Model Google Offers
This week, we're going to go on a guided tour of Google's AI Studio and the most powerful generative AI model Google has to offer, Gemini 1.5 Pro. Now, before we begin, we should clarify a few things. Google, in its fine tradition of naming things as confusingly as possible (those of you who work in Analytics, you know the joy of Google Tag Manager and the new Google Tag which is NOT part of Tag Manager despite the name) has the Gemini models themselves, but also a web interface named Gemini. Today, we'll be looking at the model, not the interface.
To access this, you'll go into Google's AI Studio, which is also confusing because it's not the Google Vertex AI Studio in Google Cloud. Google AI Studio is for prototyping stuff that you'll then later deploy elsewhere. Yeah, don't blame me, I didn't name any of this stuff. You obviously need a Google account, and to put your stuff in production, you'll need a Google Cloud account with billing turned on.
But for just casual use, this is a great place to start. For the demo use case, we'll be constructing a guide for growing an Instagram account. First, let's familiarize ourselves with the interface, which is probably my favorite for working with generative AI so far. It's not as cluttered as OpenAI's developer portal, but it's clean enough that a non-technical user can use it easily.
I'll call your attention to six key features, noted on the screenshot below.
The last part, the context window, is 1 MILLION tokens in Gemini 1.5 Pro. It's difficult to express just how important this is. The context window is a model's short-term memory, what it knows during your conversation. When we start a session with any AI tool, this is empty. As we give it prompts and interact with it, the short-term memory fills up. Over time, it gets full and when it does, the model forgets the earliest content. If you're used to the free version of tools like ChatGPT, you know this forgetting happens very, very quickly.
A million-token context window is about 700,000 words. That's 10 good-sized business books - that's how much this model can remember in the short term. That's incredible, because it means we can have it work with very large pieces of content, have extended conversations, and have it remember things we said quite some time ago. We can give it lots of reference material to think through and still not fill up the memory.
Let's put this into action. Earlier today, my martial arts teacher, Mark Davis of the Boston Martial Arts Center, asked me for some help growing our school's Instagram account (go follow them if you like). Now, I am not an Instagram expert. One glance at my own Instagram account makes it clear it's not my specialty. But there are lots of people who share lots of ideas on the topic. If we could gather up the data that exists publicly, distill it, synthesize it, and summarize it, could we come up with a highly effective set of strategies and tactics?
Here's what I did: I found every podcast on the topic of growing your Instagram account in 2024 on YouTube and fed them all to OpenAI's Whisper transcription software, run locally. This costs nothing because it uses your computer to do the processing. Now I've got a folder full of podcast transcripts. (I suppose I could have just gone to the podcasts' sites themselves for the transcripts, but it's easier to just let AI do this).
I'll take each transcript and load it into Gemini 1.5, asking it to summarize the transcript this way:
You are an accomplished professor and researcher at MIT. You sit on review boards, referee panels, and peer review committees. You review academic papers and publications.
One of your skills is a technique called paper to protocol, in which you digest a paper's findings and translate those findings into a protocol, a series of actions an individual can take based on the research.
For example, if a peer-reviewed paper finds that reducing sodium intake helps improve health, you would create a protocol that says to limit your sodium intake.
Or, if a peer-reviewed paper finds that using adverbs less in advertising copy is more effective at driving sales, you would recommend a checklist for removing adverbs and replacing those adverbs with another part of speech.
Protocols should be firmly grounded in the research, backed up by statistically meaningful conclusions from the research.
Protocols should be action-oriented; they should provide clear, unambiguous directions about what someone should do or change in their behaviors to achieve results aligned with the findings of the paper.
Today, you're going to read through a transcript discussing best practices and research around Instagram and growing Instagram accounts. You'll summarize the information you read into a set of protocols. First, you'll make an outline of the key findings as presented in the transcript. Then, of those findings, you'll distill them down to protocols.
Here's the format to use:
""" Finding Title
Findings:
Protocols:
Here's a quick look at the process as Gemini chews through each transcript:
After Gemini processes all the transcripts, I asked it to do the following:
Great. Now that you have consumed all these transcripts, categorize all the advice they give and restructure all the advice and protocols into one master outline.
And here's what Gemini spit out, distilling down all that information:
Now here's the thing: if you know Meta, and you know their transparency center, they talk about how their systems work at a high level. Lots of people talk about the Instagram algorithm, but very few seem to know how the system overall works, even though Meta has told us. If we add Meta's documentation to the mix and tell Gemini to resolve conflicting advice by leaning in favor of what Meta has told us about their own system, we get a comprehensive list of recommendations that's more balanced and aligned with how Meta says their systems work.
Master Outline for Instagram Success in 2024: Synthesizing Expert Advice and Meta Documentation
This master outline combines insights from expert advice in the transcripts with official documentation from Meta to provide a comprehensive and actionable Instagram strategy for marketers in 2024. In cases of conflicting information, Meta's documentation is prioritized as the source of truth.
I. Account Setup and Optimization
II. Content Strategy and Creation
III. Engagement and Growth Strategies
IV. Additional Considerations
This is helpful. Here's the important part in this experiment. Take a look at the bottom of Google's AI Studio:
I have consumed 76,716 tokens in this chat. That exceeds what the consumer version of Gemini can handle by about double. It's more than halfway to what the paid version of ChatGPT in the consumer interface can handle. And it's 7.3% of what Gemini 1.5 can handle. If I wanted to, I could go grab a hundred more podcast episodes or YouTube videos about Instagram strategy and add them into the short-term memory, coming up with the absolute ultimate guide to Instagram - or any other topic.
That's the power of a huge context window, of a huge short-term memory. We can take the power of large language models along with vast amounts of data we provide and turn that data into useful insights and action steps to be taken.
Now, after all this, I'm still not an Instagram expert. The synthesized advice that Gemini helped me to assemble is still only advice. I or my teacher still need to do it, to take action on it, and to bring it to life. That's the hard part - advice is easy, execution is difficult. But in terms of getting advice in the first place, this makes things much faster.
Let's briefly talk about what we didn't do in this experiment. I didn't spend a lot of time vetting the podcast episodes or transcripts. I went off popularity, which episodes in 2024 got the most attention. Popularity doesn't mean correctness, so if you're using this strategy for high-value, high-risk endeavors, you'll want to curate the source data better.
I didn't run this by a human subject matter expert. As a marketer, the advice looks generally correct, but if this were a high-stakes engagement, I'd want to run it past a real Instagram subject matter expert for their take on it - especially what's missing.
And finally, I didn't evaluate my teacher's actual Instagram profile against this list of best practices, because this is a newsletter about using a large language model with a very large context window, not about Instagram best practices. I'll be sending him the list of best practices, and this video walkthrough, but we would spend a lot more time with each of the best practices than we did.
Now, I turn this over to you. Take this advice and run with it in your industry, at your company, with your content. Get a comprehensive corpus of top content about any given subject and use a language model to consolidate, interpret, and distill it down into something you can take action on. Convert information into action, and reap the benefits of taking action on all the advice you've consumed.
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ICYMI: In Case You Missed it
Besides the new Generative AI for Marketers course I'm relentlessly flogging, I did a piece this week on the two things you'll need to truly succeed in the long-term with generative AI.
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See you next week,
Christopher S. Penn
Excited to explore Gemini 1.5 and its potential in transforming information into actionable insights! Can't wait to learn more about leveraging AI for mastering topics like Instagram marketing.
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