You might be unknowingly training your AI to be less competent, and that’s a wild thought!

Every time we accept those slightly off sentences or fluffy corporate jargon because we’re too busy to fix them, we’re basically telling our digital assistants that it’s all good. This episode dives deep into why our AI outputs might be drifting into the land of the vague and useless, and guess what? It often comes down to our own habits!

We’ll explore how we can take the reins back and make sure our AI stays sharp and true to our voice. So, let’s get ready to unpack this quirky problem together and find out how to keep our AI from becoming a sleepy intern!

And, just a heads-up, this fun exploration is AI generated based on insights from Heather Masters' LinkedIn newsletter - Start with AI.

This Deep Dive podcast is AI generated from the Start With AI Newsletter on LinkedIn - linkedin.com/newsletter/start-with-ai

The Detail:

Have you ever had that moment when your AI assistant seems to have lost the plot?

You type in a prompt that once produced gold, and now you’re staring at gibberish like, “What happened?” Well, you’re not alone!

In this episode, we chat about how little slips in our engagement – like accepting vague or fluffy outputs – can lead to a real mess. Heather Masters’ LinkedIn newsletter provides an insightful framework, revealing how our complacency can train AI to churn out bland, corporate-speak nonsense instead of the vibrant, engaging content we crave.

We break down the mechanics behind this drift, explore the psychological traps we fall into, and share techniques to reclaim control over your AI outputs.

Oh, and buckle up, because we’re also shining a light on how to start fresh and set your AI back on track without losing your unique voice in the process!

Chapters:

• 00:06 - The Impact of Our Choices on AI Quality
• 02:14 - Understanding AI Degradation
• 07:54 - Understanding AI's Limitations with Language Models
• 11:21 - Resetting the Conversation: Avoiding the Meta Level Trap
• 13:34 - Understanding Latent Spaces and Effective Communication
• 19:12 - Navigating AI and Personal Voice

Takeaways:

• You're likely training your AI to be worse by accepting vague or fluffy outputs, which is a real bummer.
• Accepting mediocre AI responses because you're busy can lead to a gradual decline in quality, turning your assistant into a sleepy intern.
• To combat AI drift, it's crucial to provide specific, structured examples rather than vague instructions, ensuring the model stays aligned with your voice.
• When correcting AI outputs, remember to anchor back to your original goals; otherwise, you risk losing the plot entirely!
• If you're using AI tools to save time without actively engaging with the outputs, you're risking the dilution of your unique professional voice.
• Starting afresh with AI requires you to lead with concrete examples and evidence instead of vague instructions to ensure effective recalibration.

Companies mentioned in this episode:

• LinkedIn
• Heather Masters
• StartWithAI Online
• Claude

You know, it's wild to think about, but you are probably actively making your AI worse.

Like every single time you accept a slightly weird sentence or some fluffy corporate buzzword just because you're too busy to fix it.

Right, because we're all rushing.

Exactly.

But by doing that, you are literally training your digital assistant to be incompetent.

Just think about that for a second.

Have you ever had that incredibly frustrating moment where you're sitting at your computer, you know, staring at the screen and you realize that the AI tool you rely on every single day is suddenly getting worse?

Oh, yeah.

Like it's just degrading right in front of you.

Yes.

You type in a prompt that used to work flawlessly and what comes back is just absolute garbage.

And you sit there thinking, like, am I going crazy?

Did they push a bad update?

Is the machine literally breaking down?

Well, I think it's a completely modern frustration.

Right.

The slow degradation of a tool that we all just assume is static.

Yeah, because one day you have this highly capable digital assistant and then I. I don't know, a month later, it feels like you're working with a sleep deprived intern who has completely forgotten everything you spent the last six months teaching them.

Well, I am here to tell you that you aren't crazy.

It is a massive hidden productivity killer.

And if you are using AI to get your work done, it is happening to you right now.

Which is why we're digging into this today.

Exactly.

We have a fantastic source grounding this deep dive today.

Is just a brilliantly honest framing of the problem.

It really is.

So the mission of this deep dive today is to unpack exactly why our AI outputs degrade over time into this useless, fluffy text.

We're going to explore the underlying mechanics of why it's usually, well, our own fault, unfortunately.

Yeah, unfortunately.

And most importantly, we're going to break down the specific structural techniques you can use to take the steering wheel back.

Because moving past the phase of just, you know, marveling that AI works at all, it requires us to enter a phase of long term management.

Right.

And managing these models requires understanding how they learn from our behaviors, especially the behaviors we don't even realize we're exhibiting.

Okay, let's unpack this, because I want to start by looking at the symptom before we diagnose the disease.

Like, what does this AI drift actually look like?

Out in the wild.

Well, Heather opens her newsletter with this really striking anecdote about a user in an AI group.

Oh yeah, the max plan subscriber.

Exactly.

So this is someone on a max plan, meaning they are a heavy paying user, not just a casual dabbler.

And they posted this desperate message saying, I've gone from loving Claude to hating it.

Which is such a harsh pivot for a premium user to make.

It really is.

Yeah.

They explained that they had like six months of work that had just quietly stopped being useful.

Creative writing projects were completely abandoned.

Workflows that used to run incredibly smoothly now required this constant painful correction.

Just constant micromanagement.

Right on things the AI had previously just understood without being told.

But the real gut punch was a comment someone else left on their post.

Aw, this was brutal.

It was the reply just said you probably trained it into producing rubbish.

Consider starting over.

Brutal but deeply honest.

Now, to be fair to the listener out there who might be experiencing this, we should validate that sometimes the tools genuinely do change under our feet.

Oh, absolutely.

Model updates happen, which shifts the baseline behavior.

But more commonly we run into technical limitations like context windows hitting their limit.

Hold on.

Before we blame ourselves entirely, let's demystify that for a second.

People throw the term context window around a lot.

For anyone who isn't deep in the AI weeds.

What is that actually doing to our workflows?

Sure.

So think of a context window as the AI's short term memory capacity for a single conversation.

It's measured in tokens, which are essentially just pieces of words.

So if a model has a small context window and you have been chatting with it for, say, three hours about a complex project, the earliest parts of your conversation literally fall out of his memory.

Wow.

Yeah.

It is mathematically incapable of remembering the foundational instructions you gave it at 9am once you reach 1pm that is a hardware and software limitation.

And it's absolutely not your fault when a project loses its thread that way.

So it literally just drops the old stuff to make room for the new stuff.

That makes sense.

But that's just the technical side.

Heather's newsletter is getting at something much more insidious.

What's fascinating here is the psychological trap that Heather identified in her own workflow, which is completely separate from those hardware limits.

Right.

She noticed the second phenomenon happening with her Sunday newsletter when she first started using AI to draft it.

The output was great.

It was recognizably hers.

But over time, it started getting what she called fluffier.

Fluffier.

I mean, we all know exactly what that looks like.

It's that vague, aspirational, corporate speak.

Yeah.

The language softens.

Phrases start appearing in the text that a real human being would absolutely never use in casual conversation.

It's the kind of writing that sounds very meaningful on the surface, but when you actually parse the sentence, it conveys zero actual information.

Exactly.

Her newsletter was slowly drifting away from her unique voice and gravitating towards something entirely generic.

Why does it do that, though?

Like, if left to its own devices, why does an AI default to generic corporate fluff instead of defaulting to, say, Shakespearean English or technical jargon?

It all comes down to how these models are trained before they ever reach the public.

There's this process called Reinforcement Learning from Human Feedback, or RLHF.

Okay, RLHF.

Right.

Essentially, armies of human testers reward the AI for being harmless, polite, and universally agreeable.

Ah, I see.

So the model learns that strong opinions, unique stylistic quirks, or, you know, edgy humor might offend someone or be flagged as a bad response.

So it mathematically regresses to the mean.

It smooths off all the edges.

The fluff is just the safest possible combination of words the AI can predict.

That is wild.

The fluff is a defense mechanism.

The AI is just trying not to get in trouble.

Exactly.

And the core revelation Heather had was about her own role in enabling that defense mechanism.

She kept publishing the fluffy text because she was busy.

The output was mostly fine.

You know, maybe an 80% match for what she wanted.

Right.

Good enough.

Good enough.

So.

So she didn't correct the 20% that was off.

She realized she was subconsciously treating the AI as if it knew better than she did.

She made one small acceptance of a weird, overly formal phrase, then another, and suddenly she had a whole body of work that wasn't quite hers anymore.

Because the AI learns from your complacency.

Like Heather says, silence reads as agreement, and agreement repeated over weeks and months is a hard instruction.

Wow.

If you accept something that is 80% right and 20% off because you're rushing to meet a dead, you are actively teaching the neural Network that the 20% deviation is your new preferred baseline.

So you do that a dozen times, and that 20% deviation becomes the center of the target.

This makes me think of using a gps.

When you're driving in an unfamiliar city, you're following the blue line, and you take a slight wrong turn, but instead of letting it reroute you back to the main highway, you just keep driving because you assume, hey, Maybe the GPS knows a shortcut.

Right, you just trust it blindly.

Yeah, you silently agree with the new path, and you keep doing that until suddenly you are comm completely lost in an industrial park you don't recognize, wondering how you got there.

That's a perfect analogy.

Yeah, the machine doesn't know it's a mistake unless you flag it.

If you keep driving, it recalculates the route based on your current trajectory, assuming that is where you actually want to go.

The AI applies that exact same logic to your text.

It assumes your silence means the current trajectory is perfect.

So if silence is the enemy here, and passively accepting the fluff is what breaks the tool, how do we actively fix it?

Do we just start yelling at the AI every time it makes a typo or uses a vague buzzword?

Well, you would naturally assume that direct, forceful correction is the answer, but this is where the mechanics of large language models get incredibly counterintuitive.

Trying to aggressively course correct a slipping AI midstream can actually ruin your underlying prompt entirely.

Wait, let me challenge that, because I'm paying 20 bucks a month for a top tier model, right?

They boast about these massive hundred thousand token context windows.

The marketing says it literally has a memory large enough to hold an entire novel.

Yeah, the marketing is very effective.

So why does it forget my overarching project rules just because I asked it to fix one paragraph of fluffy text?

I mean, that feels like a massive design.

Flawless.

Not a user error.

It definitely feels like a flaw until you understand how attention mechanisms work under the hood.

It's not about memory in a human sense.

It's about computational weight.

Okay, computational weight.

Right.

These models use an architecture called a transformer, which assigns mathematical weight or attention to the words in your prompt.

When you say that paragraph is terrible, fix the tone.

The model's attention mechanism spotlights your immediate complaint.

Oh, I see.

So the spotlight shifts entirely to my negative feedback.

Yes, the spotlight moves to the immediate correction, leaving the original complex prompt in the dark.

Wow.

Heather actually shares a second anecdote from that same AI group that illustrates this beautifully.

Another user had built a very careful, sophisticated prompt for a complex problem.

They had clear structure, stated conditions, defined roles for the AI to play.

Really robust setup.

Very robust.

And the initial response was great.

They asked for more depth.

Still great.

But then on the third or fourth turn, the AI made a logical error.

So the user points out the error, they yell at the gps, they pointed.

Out the error, and the AI, as they're programmed to do by that RLHF Training we talked about immediately apologized.

It accepted the correction and moved forward.

But the user noticed that the original carefully crafted thread, all those stated conditions and structural rules, had been quietly abandoned.

It just dropped them.

Just dropped them.

The conversation shifted.

It was no longer about solving the complex business problem.

It had become a conversation entirely about getting back on track.

Oh wow.

A conversation about getting back on track rather than actually doing the work.

I have been trapped in that exact loop.

We all have.

You spend five subsequent prompts arguing with the machine about what it just did wrong, and by the end of it, neither of you remembers what the original project even was.

Exactly.

In natural language processing terms, you have moved from the content level up to the meta level.

The meta level.

Right.

The content level is the actual work you want done, like writing the code, drafting the email, analyzing the data.

The meta level is a discussion about how the work is being done.

I get it.

And once you force the AI into the meta level by aggressively correcting a small mistake, its primary objective becomes appeasing your immediate frustration, not executing the original multi step plan.

So I am essentially spending my tokens and my time arguing about the mistake.

And the AI is dedicating all of its processing power to apologizing rather than remembering the foundation.

Yes, exactly.

So how do we reset that frame without losing the plot entirely?

You have to re anchor to the original goal at the exact same time you make the correction.

You cannot simply say, that's wrong.

Fix it.

Okay, so what do you say?

You have to say something like, that's not where I'm going.

What I need is X.

Can we return to that original structure?

You have to explicitly pull the destination back into the immediate spotlight of the context window.

Okay, that makes sense.

The destination must stay visible in your current prompt or it will be mathematically replaced by whatever complaint you just typed.

So you can't just tell someone they're doing it wrong.

You have to remind them what right was supposed to look like.

Exactly.

But let's look at the worst case scenario.

What happens if you're too far gone like that first user who had six months of drift?

If you've been stuck in the meta level trap for weeks and the AI is just churning out absolute corporate fluff, you have to start over.

Right?

You do.

And Heather talks about how to do a fresh start correctly.

Because apparently most of us completely botched the reset.

Oh, we completely botched it.

Because human instinct when frustrated is to become highly dictatorial.

Like when people finally snap and open a fresh chat window, they write these massive aggressive Lists of instructions.

Do not do this.

Never say that.

Use this specific tone.

Be professional but friendly.

Avoid jargon, which never seems to work.

It always just spits back a slightly different flavor of robot speak.

Right.

And the fundamental rule Heather lays out for starting over is critical here.

You must begin with evidence rather than instructions.

Evidence over instructions.

Why does an instruction like be professional think fails so spectacularly?

Well, consider the math behind it.

Words like professional or warm or punchy are highly subjective.

In a large language model's latent space, which is the multidimensional map of billions of parameters where it stores information.

The concept of professional is a massive, vague cloud.

Right.

It means a million different things.

Exactly.

When you instructed to be professional, you are essentially throwing a dart at that massive cloud blindfolded.

Okay, here's where it gets really interesting.

Let me try an analogy to see if I'm grasping this latent space idea.

Go for it.

Relying on instructions feels like walking into a restaurant kitchen and giving a chef a really vague subjective demand.

Like make this dish taste warmer.

Yes.

The chef is going to look at you and guess what warmer means.

Maybe they add cinnamon.

Maybe they dump in chili powder.

Maybe they just put it in the microwave.

They're guessing at your subjective demands definition.

That is the exact dynamic.

You are forcing the system to guess which part of the warm cloud you mean.

But providing evidence, that is like handing that same chef a master recipe of your best dish and saying, look at this.

Notice how I always brown the butter first before adding the sage.

That specific action is the flavor profile I want.

You remove the guesswork entirely.

That analogy captures the necessity of structural precision perfectly.

The newsletter points out that instructions merely tell the AI what you want in theory.

Examples, your master recipe.

They show the AI the rule.

Working in practice.

Right.

Giving the AI three examples of your past writing doesn't just give it a vibe.

It gives the model exact mathematical coordinates in that latent space to mimic the difference in what a model actually learns from a vague instruction versus a concrete example is statistically massive.

So we have to be painstakingly specific.

We have to do the heavy lifting up front.

Yes.

Heather quotes this directly in the source material.

You don't say, I want it warmer.

You say, I never use passive constructions in my openings.

Here are three examples of what I mean.

Okay.

That's concrete.

Very concrete.

If you want precision back from the machine, you have to feed precision into it.

Vague feedback will inevitably Produce vague recalibration.

So if I'm starting a fresh project, I shouldn't just ask for what I want immediately.

I need to bring my absolute best work to the table before I ask it to do anything.

You bring examples of the work you are most proud of, but you don't just dump a massive block of text into the prompt window and say, sound like this.

No.

You have to name specifically what makes each example work.

You point out the structural moves, not the feeling, the actual mechanics of the writing or the logic of the code.

Right.

You open the new conversation by calibrating that starting point before you allow the AI to generate a single word of new content.

You lock in the baseline before the drift has anywhere to begin.

That is such a powerful shift in mindset.

You aren't just a user typing queries into a search box.

You are actively calibrating a partner.

Exactly.

But listening to all of this digging into latent spaces and attention mechanisms, it strikes me that there's a massive underlying assumption here.

We're talking about the mechanics of prompting, giving structural examples, and refusing to accept fluffy language, Right?

But this whole deep dive ultimately points to a deeply human prerequisite.

You can't protect something if you don't think fully understand it yourself.

Ah.

If we connect this to the bigger picture, that is the exact warning Heather is issuing.

Her initial mistake wasn't a technical error with a prompt.

Right.

Her mistake was handing over the wheel without noticing because she hadn't firmly defined her own voice in the first place.

The underlying existential question of her whole newsletter is, do you actually know your own thinking, your own voice, or your own approach well enough to protect it when the machine inevitably starts pulling in another direction?

Wow.

That is a confronting question for anyone who uses these tools daily.

Because if I'm honest, a lot of times I use AI specifically because my own thinking is feeling a little muddy and I want the machine to clear it up for me.

We all do that.

But if I don't know what my unique value is before I open the chat, the AI is just going to overwrite me with the average of the Internet.

The real work happens before you ever turn the computer on.

It's intimately knowing your own methodology.

And Heather stresses that this isn't a one and done setup.

Both the AI landscape and your own thinking are going to shift faster than you account for.

The voice or the approach that you fed into the machine six months ago might not be your best version anymore.

That makes total sense.

These AI agents, these custom instructions we build, they have to be treated as living documents.

The practitioners who actively maintain them are the ones who stay in the driver's seat.

So what does this all mean for you?

Listening right now.

If you're listening to this and thinking about the custom prompts you built back in January that are now completely useless, you need to recognize the trap.

Yep.

If you are using AI purely to save time, and you aren't rigorously policing the outputs, you're risking the total dilution of your own unique professional value.

You are training the machine to make you sound like everyone else.

Which defeats the purpose entirely.

Exactly.

And if this is resonating, if you recognize that gradual creep of fluffiness in your own emails or reports.

Heather Masters actually offers a dedicated resource for this.

She runs a five day email course over at StartWithIDA Online, designed specifically for practitioners who want to start this foundational work and, as she puts it, stay human while using these tools.

It's a highly recommended starting point for anyone who feels like they've slowly slipped into the passenger seat of their own workflow.

So, to quickly recap our mission today, we learned that with AI, silence is a direct instruction.

Right.

If you accept the fluff, the model's reinforcement learning guarantees you will get more fluff.

We learned that when you have to make midstream corrections, you can't just point out the error and trigger the meta level track.

Go and lose the plot.

Exactly.

You have to explicitly re anchor the AI back to your original goal so the attention mechanism doesn't drift.

And finally, when you inevitably have to start over, always lead with hard evidence and structural coordinates, Never just vague subjective instructions.

This raises an important question, though.

Something that builds on everything we've discussed today about protecting your methodology.

Oh, I love a good curveball to end on.

What are you thinking?

Well, the source heavily emphasizes locking in your voice, Right.

You feed the AI your best structural examples to prevent it from drifting away from your current standard.

Yeah, locking it in.

But consider the long term implication.

If you are constantly enforcing your past structural habits on the AI to keep it perfectly aligned with you.

Are you artificially freezing your own voice in time?

Oh, wow.

By being so incredibly rigid to stop the machine from drifting, might you accidentally be preventing yourself from organically evolving and changing your mind as a thinker?

Oh, that is fascinating.

Are we trapping ourselves in our own greatest hits just to keep the machine in?

Exactly.

By forcing it to sound like the me from six months ago, I never get to figure out who I sound like today.

That is a brilliant thought to chew on.

You can't just let the AI Drive, but you also have to make sure you aren't just driving in endless circles in your own driveway.

Well said.

Thank you so much for joining us on this deep dive.

Keep your hands firmly on the steering wheel, stay curious, and we will catch you next time.