Stop Using AI Like Google: Build a Marketing + Sales Agent Instead

Hi, and welcome to Jam Packed Business Made Easy. My name is Jam Anderson and I'm the CEO of JAM AI and today we're practicing exactly what we teach. You're about to listen to a podcast that's truly automated with our AI hosts, who are now our new friends. We also would like to invite you to listen to our podcasts that come out every Friday, where Flora is interviewing entrepreneurs just like you or exploring authentic challenges and discovering practical solutions together. Speaking of community, if you're looking for a community, you can find us on the Skool platform. Just search for the number one community for entrepreneurs. All right, let's dive in.

I have to admit, I had a bit of an existential crisis reading the source material for today.

Oh, yeah, seriously.

We're looking at this guide, Mastering the Art of Generative Search Prompts, and it really forced me to look at my own habits.

I think I know where this is going.

I'm still communicating like a caveman. I mean, for all the tech I talk about, my prompts are basically grunts.

It's the Me Tarzan Eugene approach to artificial intelligence.

It really is. Yeah. I literally caught myself this morning. I was trying to fix a spreadsheet and I just typed Excel formula, Vlookup error.

Just keywords.

Just keywords. Fragmented thoughts. I'm treating this using know unbelievably advanced neural network like it's Alta Vista in 1998.

You and pretty much everyone else. And that's really the core tension of the article we're diving into today.

Right.

The headline is how to stop using AI like Google. But the Deep dive here is about, you know, rewiring a 20 year old cognitive habit.

A habit we've all been trained to have.

Exactly. We're all conditioned to strip away nuance because search engines just couldn't handle it. But this guide argues that when you carry that keyword baggage over to generative AI, you're not just being inefficient, you're actively breaking the tool.

Okay, see, that's the part I want to unpack first. Breaking the tool sounds. Well, it sounds dramatic.

It does. But the author makes a really compelling case for it. They argue that Google speak, you know, keywords, fragments, it's designed for retrieval, for.

Finding something that already exists.

Right. But generative AI is designed for synthesis, for creating something new. And those are two totally different cognitive tasks.

Okay, let's dig into that. Retrieval versus synthesis.

Sure. So let's look at the architecture for a second just to explain why this matters so much. When you use A traditional search engine. Let's call it the retrieval model.

Right.

Like Google, you're basically asking a librarian to run down an aisle and grab a specific book. If you show whether Tokyo, the librarian knows exactly which book or which page to grab.

It's a matching game. My keywords have to match the words in the document.

It's deterministic. Exactly. But a large language model, the generative model, it doesn't have a library of documents.

It has what?

It has a probabilistic map of language. It's just predicting the next most likely word or token.

So when I type weather Tokyo into an LLM, it's not finding a report.

No. It's generating a sentence that statistically follows those two words. It's building the answer from scratch.

Which explains why sometimes it just makes stuff up. It hallucinates.

Right. But the guide points out something a little more subtle than just hallucination. It talks about the average trap.

The average trap.

If you give the AI a generic keyword style prompt, you will get a generic average output because you're not giving.

It enough to work with.

Precisely. The model is trained on the entire Internet. The brilliant stuff and, you know, the really mediocre stuff. If you provide low resolution input like marketing strategy saws, the model has no signal to narrow things down. So it just reverts to the mean. Yeah, it gives you the most statistically probable answer, which is usually a very bland Wikipedia style summary.

So by being lazy with my prompt, I'm basically forcing the AI to be mediocre.

You got it.

I'm driving the Ferrari in first gear because I'm afraid to touch the stick shift.

That's the perfect analogy. You're under specifying the request. And the guide argues that to really master generative search, you have to completely invert your behavior.

So instead of stripping context away, you.

Have to pile on the context. You have to add it in to help the machine triangulate a specific high quality answer.

Okay, so let's get into the how of this. Let's get practical. The source material breaks this down into what it calls the art of prompts.

Right.

And I'm actually glad they used the word art, because frankly, prompt engineering always sounded a bit intimidating.

Like you need a computer science degree.

Yeah, like I needed to learn Python just to ask a question. But this guide simplifies it into two main. Context and intent.

Context and intent. And it sounds simple, but in practice, it actually takes a lot of mental friction to do it. Right.

Let's start with context. In the Google World context is basically irrelevant. The search engine doesn't care if I'm a CEO or an intern.

Nope. The results for Q3 earnings report are the same links for everybody. Right, but for a generative model, context is everything. It's the coordinate system. Yeah, the guide suggests that context isn't just about the topic, it's about the whole scenario.

Okay, so I actually tried to apply this after I read the article. I needed to draft a difficult email to a client.

Okay.

You know, pushing back on a deadline, my old habit would have been typing what Email delay project template, which would.

Give you that generic robotic dear sir or madam template that sounds like it was written by a lawyer from 1985.

Exactly. So applying the context rule, I stopped and I wrote this instead. I'm a senior account manager. I have a good relationship with this client, but they keep expanding the scope.

Scope.

Great. I need to tell them the deadline is moving, but I want to frame it as a quality assurance measure, not a failure on our part.

See the difference there? It's huge. You gave it the Persona Senior account manager.

Right.

The relationship dynamics. Good relationship. But scope creep. And the strategic angle, Quality assurance. Now, the model isn't just predicting the next word for email, it's predicting the.

Next word for a diplomatic negotiation strategy.

Exactly.

And the result was. I mean, it was actually usable. But here's the pushback I hear from people, and honestly, a part of me feels it, too.

O on.

That took me, what, 45 seconds to type out the keyword search takes three seconds. Is the efficiency gain of AI actually real if I have to write a mini essay just to get started?

That's the classic efficiency paradox. You save 40 seconds on the prompt. Sure, yeah, but then you get a generic output that you have to spend the next 20 minutes rewriting, editing and trying to humanize. The source argues you're just shifting the workload. You used to spend your energy on the backend, sifting through a dozen links, synthesizing the info, writing the draft. Now you have to spend that energy.

On the front end, defining the problem clearly from the start.

It's front loading the cognitive load. If you're muddy in your request, the AI is going to be muddy in its answer.

So garbage in, garbage out has become what? Vague in, vague out.

Precisely. And this leads us right to the second pillar. Intent.

Intent. Okay, how is this different from context?

It's distinct. Context is the background. The who, where, and why. Intent is the output, the what and the how. Okay, in the Google era, your intent was almost always just Find, find the page, find the fact.

Right. I'm looking for a destination, a link.

But in generative search, your intent can be transform, summarize, critique, brainstorm format. The guide really emphasizes that most people fail here because they don't transition. Treat the prompt as a formatting instruction.

They just treat it as a question.

Yes. Can you give me a concrete example of that?

So, let's say you're a developer and you've got this messy error log. A question approach is just, what does this error mean?

And the AI will tell you, it's fine, it's okay.

But a master prompt with clear intent would be something like, analyze this error log, identify the three most likely root causes, and propose the code fix for each in a Python block formatted for a junior developer to understand.

Oh, wow. So you're not just asking for the answer. You're actually designing the document you want to receive.

You are programming the output. You're saying, don't just give me the information, give me the information in this specific container, whether that's a bulleted list or a slide deck outline or a Python script.

And if you don't specify the intent, the AI just defaults to what, a.

Conversational paragraph, which is almost always the least useful format for actual work.

Right. It's almost like we need to stop thinking of it as a chatbot and start thinking of it as a remote worker.

That's a great model. If I sent a messy pile of data to a remote analyst and just.

Said thoughts, you'd get a messy email back.

But if I said, turn this into a table comparing year over year growth, I'd get a table.

That is the perfect mental model. The guide uses the analogy of a research assistant versus a a librarian. But I'd go even further. It's like having a genius intern who has read every book in the world, but has absolutely zero social awareness or initiative. If you don't tell them exactly what to do, they will just stand there and recite facts at you.

Recite facts at you. That feels painfully accurate to some of my early experiences with these tools, I.

Think, for all of us.

But this brings up a friction point for me. The guide talks about this art of prompts like it's a static thing. You write the perfect prompt, you hit enter, you get gold.

Right?

But in my experience, even when I use good context and intent, that first result is often, you know, it's about 80% there.

And that is where the Google habit is the absolute hardest to break. We are conditioned to believe that the search Results page is the end of the transaction.

Yeah. You search, you see links, you click away. Done.

The transaction is over. If you have to search again, you feel like you failed or Google failed.

Right.

The source material argues that in generative search, the first output is merely the first draft.

So stop using AI like Google. Also means stop treating the result as final.

Yes. The power users, the ones really mastering this, they don't spend 10 minutes crafting the one perfect opening prompt. They write a good one, get a result back, and then they steer.

They iterate.

They say, okay, that's good, but you missed the risk Factors. Rewrite section 2, focusing only on regulatory risks. Or make this more concise.

It's collaborative editing.

It is. And this is a huge behavioral shift. We aren't used to talking back to our software.

No.

If Microsoft Word does something annoying, we just sigh and fix it manually. If Google gives us bad links, we change our keywords. But here you're actually supposed to say, no, that's not quite right. Try it again. This way.

I love that distinction. It turns the user from a consumer of information to into a director of information. But does the source talk at all about the risk of. Well, of echo chambers? If I'm constantly telling the AI who I am and what I want, aren't I just biasing the machine to tell me exactly what I want to hear?

That is a fascinating and really important layer to this. The guide touches on it briefly, but let's expand on it. If you provide too much biased context, like I believe strategy X is the best, write a report proving it. The AI, being a prediction engine, will happily hallucinate evidence to support your bias.

Because its primary goal is to please the prompt, not necessarily to tell the objective truth.

Correct. It's just completing your pattern. If your pattern is biased, the completion will be biased. So part of mastering the art is actually knowing when to remove context or to change it? Yes. Sometimes you have to say act as a skeptic. Critique my assumption that strategy X is the best.

So the art isn't just about add more detail. It's about control the lens.

Exactly. You are swapping lenses. Look at this data as a cfo. Now look at it as a customer support rep. Now look at it as our biggest competitor. A Google search can't do that.

No, it just shows you the data.

Generative AI can simulate the perspective, but you have to be the one to ask for the new perspective.

This really solidifies why the Google habit is such a huge limitation. Yeah, if I'm just keyword searching I'm stuck with one lens, the average Internet.

User lens, which is usually a lens of, you know, mediocrity and consensus.

Okay, so let's bring this down to brass tacks for everyone listening. Yeah, we've covered the theory, context, intent, iteration, but someone's listening to this on their commute. They get to work. What is the one concrete thing they should do differently today?

The guide suggests a simple stop and pivot exercise. The moment you feel your fingers hovering over the keys to type a keyword fragment.

Like Marketing Trends 2026.

Exactly. Physically, stop, take your hands off the keyboard, break the muscle memory and ask yourself one question. Am I looking for a link or am I looking for an answer?

I like that.

If you want a link, like the login page for Salesforce, use Google. It's the right tool for the job. But if you want an answer, force yourself to write a full sentence, even.

If it feels weird and slow at first.

Especially if it feels weird. Right. I need to understand the top three marketing trends for 2026, specifically for B2B tech companies, and I want you to summarize them in a memo format.

It feels like writing a brief for a colleague.

That's the mindset. If you wouldn't just shout marketing trends at a colleague and walk away. Don't do it to the AI.

You know what's really striking me as we talk through this is how much it shifts the valuable skill set of the future.

How so?

We spent the last 20 years valuing search skills, knowing how to use Boolean operators, knowing which sites were trustworthy. That was the core information age skill.

And now we're moving into what? The curation age. Or maybe the direction age.

The direction age. I like that. Because if the AI can retrieve and synthesize any fact in the universe in seconds, the value of just knowing the fact itself drops to near zero.

It's basic supply and demand. Answers are now cheap. They're instant, they're infinite. So what becomes scarce? What becomes valuable? The question. The context. The ability to frame the problem. The source material hints at this. The future really belongs to the curious, not to the memorizers.

If you can ask a nuanced, multi layered question, you can leverage the intelligence of the machine. If you can only ask Best Pizza.

Nyc, you're just a consumer of average information.

Wow. It's an economy of curiosity that is. Yeah, that's actually kind of inspiring. Makes me feel less bad about my history degree.

Finally, a use case for critical thinking.

But seriously, it does suggest that the human in the loop isn't going away, but the human's role is changing from finder to architect.

Architect is the word you are building the structure of the answer you want. The AI just pours the concrete, but if your blueprints are just scribbles on.

A napkin, the building falls down.

Exactly.

So. So the takeaway for our listeners today is pretty clear the next time you open up your AI tool of choice. Yeah, don't be a caveman. Don't just grunt keywords at the genius in the box.

Treat it like a high level collaboration, Give it a roll, give it a goal, and don't be afraid to tell it when its first attempt isn't good enough.

And honestly, playing around with context is really fun. I think I'm going to go home and ask it to explain quantum physics to me in the style of a 1940s film noir detectives.

See, now you're getting it. That's a lens shift.

I bet the explanation will stick a lot better than a Wikipedia article too.

It probably will. And that's the real power of this tool.

Thank you all for listening to this deep dive. I hope this helps you break that Google habit and start actually driving the Ferrari. Let us know what crazy prompts you come up with.

Stay curious.

We'll see you in the next one.