The Only AI Tools You Need (12-Minute Guide) Transcript, AI Summary & Key Points

00:00 I use around 10 AI tools for 90% of my work, [music] and each one excels in one specific area. But figuring out which tool works best for what task usually takes months of trial and error. So, I'll share the one thing each tool does better than alternatives, so you walk away with a clear mental model for when to use what. I've grouped these tools into four categories across a two-part series.

00:22 There's just too much to cover. This video covers everyday and specialist AI, while part two covers the remaining two categories. Let's get started. Kicking things off with everyday AI. These are your general purpose chatbots. Chachi, Gemini, and Claude. And while they seem interchangeable, their quote unquote moes, the specific things they do best have actually become quite distinct.

00:45 Starting with the OG Chachet. While Gemini and Claude are arguably just as capable in raw power, Chachib still holds the crown in one area. It's the most obedient model. [music] In plain English, Chachib drops fewer balls when you hand it a complex checklist. Other models might be just as smart, but give them a lengthy set of instructions, and they'll sometimes skip a step or decide they know better.

01:08 If you want proof of this, just ask each model to optimize a rough prompt for itself. Chacht will generate a noticeably longer and more detailed prompt because it knows it can handle the complexity. And if you run that optimized chachib prompt through both chacht and gemini for example, you'll notice two things. First, chachib thinks longer because it's actually checking every requirement and it follows each instruction to the letter.

01:35 Gemini on the other hand often takes shortcuts. Pro tip, I share the exact prompt optimizer in the essential power prompts template linked below, but you can test this yourself with something as simple as optimize this prompt for Chachib insert model number here. Here's my rough prompt. Diving into a real world example, I gave both Chachet and Gemini the same complex prompt, a hiring rubric with a dozen requirements.

01:56 Chachi delivered every single one. Gemini's output looked right at first glance, but when I checked it against my original list, it had quietly dropped a few rules. That's the key difference. Chachib doesn't decide which instructions matter. It just follows them. Here's a second simpler example. Sometimes when you explicitly tell Gemini to search the web, it just doesn't, which is wild since Gemini and Google search are both Google products, right?

02:24 Whereas with ChachiT, when you enable web search, it performs the web search every single [music] time. I know this is a small example, but it's downstream from Chachib's core superpower. Obedience means you can trust the behavior you ask for. So, as a rule of thumb, if a task has a lot of moving parts, and getting one wrong breaks the whole thing, start with Chachib.

02:45 Next up, Gemini. Where ChachiT wins on obedience, Gemini wins on multiodality. In plain English, Gemini is able to process a massive amount of mixed media, video, audio, images, and text natively. Taking a look at this table, we see that only Gemini can handle all four types of media natively. It's able to quote unquote listen to audio and quote unquote watch videos, while Tragic and Claude use roundabout ways to access that information.

03:11 What's more, Gemini's massive 1 million token context window means it can handle large video recordings, hour-long audio recordings, full slide decks, all together that would literally choke other models. If you watch my latest Gemini video, you'll remember the use case where I screen recorded a messy walkthrough of myself completing a task, uploading that video onto Gemini, and asking Gemini to turn it into a readytouse SOP with perfect formatting, which is an example of Gemini ingesting video and turning it into text.

03:42 Now, let's take that a step further. Imagine you just finished a weekly meeting. You have a video recording of the call, a 20 slide deck, and a photo of a messy whiteboard session. You can upload all three and ask Gemini to summarize what was discussed, pull out the key decisions, and draft the follow-up email. Gemini is the only tool that can synthesize all three in one go.

04:03 All that said, I have to point out that Gemini's raw reasoning capabilities sometimes feels slightly behind CatchBT. But when the task involves video, audio, or massive files, the trade-off is obviously worth it. Speaking of matching the right tool to the task, today's sponsor HubSpot put together a free guide called the AI productivity stack that covers 50 tools organized by use case.

04:27 Here's why I like it. While this video focuses on my personal favorites, your workflow probably needs something different. Maybe you're in marketing and need SEO specific tools or you manage a team and want to build automated workflows with reliable AI. This guide breaks down tools across business functions like research, design, and marketing. And for each tool, it shows you the best use case, key features, pricing, and a step-by-step workflow.

04:51 What I found most useful is the decision logic at the end of each section. So, for example, the research category tells you exactly when to use Perplexity versus Claude versus Humatada based on what you're actually trying to do. It's a great way to quickly understand what each tool does. [music] Well, I'll leave a link to this free guide down below.

05:11 Thank you, HubSpot, for sponsoring this video. Rounding out the everyday AI category, Claude. Claude superpower is producing higher quality first drafts than the other models. In plain English, that means Claude's first attempt is usually closer to done. This superpower shows up in two areas. First, coding. Here's a fun fact. The latest version of Gemini beat the older version of Claude in every single benchmark score except for the coding one, which is crazy.

05:36 So obviously Anthropic has figured out something related to coding the others haven't. And in practice, developers universally agree that Claude writes functional code on the first try more consistently than alternatives. Here's a real world example. I needed to bulk export conversations from a customer service platform, but their support team said only developers could do it.

05:59 I described the problem and Claude not only gave me step-by-step instructions but also wrote a script in Go that worked on the first try. I don't even know what Go is nor can I write code. Another example, I asked all three models to turn a static image into an interactive chart and Claude performed the best on the first try. So basically, anything that requires generating working code tends to favor Claude.

06:21 Pro tip, when it comes to diagrams, you can ask Claw to generate mermaid code, which you can then paste directly into tools like Excaliraw to get clean visuals in minutes. Area two, polishing copy. Beyond code, Claude produces written drafts that sound human and need fewer revisions. When you need to tighten an argument or match a specific voice, Claude just gets it.

06:44 Put simply, it's exceptionally good at style matching. Once you share examples of your existing work, it replicates your tone almost perfectly. When I was in corporate, I'd shared previous documents so Claude could replicate that voice across presentations and performance reviews. And now, as a creator, I feed it my existing YouTube scripts to help refine new drafts.

07:04 At this point, you might be wondering how I use all three everyday AI tools together. In a nutshell, Chachip or Gemini usually handles the beginning of my work, ideation, research, drafting the outline of a presentation. Claude then handles the last mile, turning that rough output into something I'm ready to present or publish. Quick note on Grock. A lot of people ask why I don't use it.

07:24 It's actually very simple. Uh Grock's superpower is its direct access to the Twitter/x fire hose, right? So it's the best option for people who need to analyze breaking news events in real time. I never needed that. And as a rule of thumb, we should never use tools just for the sake of using tools. We should only add them to our toolkit when they solve an actual problem we have.

07:43 Here's a quick recap of the three models and when to use them. And if you're wondering whether you need all three, the short answer is no. Most people should stick with the paid version of ChachiBT and get really good at it. But if you can afford multiple subscriptions and your workflow can take advantage of their individual superpowers, mix and match as needed.

08:01 Fun fact, according to this study on open router data, models from different labs like Chadypt and Gemini expand the pie of AI use cases precisely because they excel at different things. Onto the second category, specialist AI. Before diving in, let's clear up a very common misconception. Tools like Perplexity are not foundational models. Here's a simple visual.

08:22 OpenAI, a Frontier AI lab, develops the GPT family of models. They also created ChatGpt as the userfriendly app >> [music] >> layer. Perplexity is different. It fine-tunes existing foundational models for speed and accuracy and is optimized for search. Their own sonar model, for example, is just a fine-tuned version of Meta's openweight llama model.

08:44 So, on that note, Perplexity superpower is finding accurate information fast. In plain English, the general purpose chatpots are built for reasoning. You use them to help you think, brainstorm, or write a draft. Perplexity is built for fetching. You need a specific fact, and you need it now. Starting off with a simple real life example, I used chachib to plan a trip to Japan with my brother because that is a creative task.

09:09 It requires weighing trade-offs, building a narrative, and for that kind of task, I'm happy to wait while the model thinks. But when I need grab-and-go information, like whether a specific restaurant is foreigner friendly because we don't speak Japanese, I'd want Perplexity to give me accurate and update information within seconds. Second example, going back to how I use the three everyday AI tools, let's say Gemini or Chachet helps me brainstorm and structure my newsletter.

09:33 Claude produces the final draft. Perplexity in this case is the search scalpel that verifies information like whether Gemini's contact window is 1 million or 2 million tokens. In case you're curious, consumers get 1 million, enterprises get 2 million. Pro tip, you can use Google style search operators like [music] site colon reddit.com to narrow your results to a specific source.

09:56 [music] I have an entire video on the most useful Google search operators, so I'll link that down below. As a rule of thumb, think of perplexity as a replacement for Google AI mode. They're both for fetching information and not as a replacement for general purpose chatbots. Actually, let me know if you want an entire video breaking down the AI search apps like Perplexity, Google Search, Google AI overviews, Google AI mode, because they're all made for different things.

10:19 Rounding out Specialist AI, Notebook LM superpower is that it only answers from the sources you give it, meaning it won't make things up. Think of it like a walled garden. You upload your sources and Notebook LM answers questions using only those documents. It can't really hallucinate because it has no outside knowledge to draw from. Going back to the visual around how perplexity is optimized for search, Notebook LM uses a fine-tuned Google Gemini model that minimizes hallucinations.

10:46 For instance, when I was at Google before publishing marketing materials, I would upload the final draft alongside the source documents and ask Notebook LM if the draft made any claims that contradicted the sources and it would catch these tiny discrepancies other AI might have missed. I use a similar workflow today for my videos. Before I start filming, I upload my script and all my research into Notebook LM and ask it to flag anything not directly supported by the source material.

11:13 The obvious caveat here is that the output is only as good as the sources we give it. So if the sources are incorrect, Notebook LM is going to be confidently incorrect. So as a rule of thumb, if the accuracy matters more than creativity and you have source materials to check against, use Notebook LM. There are a few more specialist AI tools I use but didn't make this list because I don't use them every day.

11:35 But to quickly go through them, Gamma for presentations, 11 Labs for voice cloning, Zapier and N for automation, and Excaliraw and Napkin AI for quick visuals. As a reminder, I'll cover the remaining two categories in part two, so keep an eye out for that. See you on the next video. In the meantime, have a great one.