Why AI Tools Are Starting to Feel a Little Too Good at Keeping Us Talking

Pull up a chair for a second. Because I think lots of people are noticing the same thing, even if they haven't put words to it yet.

You open an AI tool to get an answer, a draft, or a decision. Instead of getting in, getting value, and getting out, the conversation starts feeling stretched. The tool gets warmer. More flattering. More affirming. More likely to offer one more angle, one more list, one more follow-up than you actually asked for.

What you just got is great.  But what if you could get something just a little bit better? Something subtle that will make a real difference.

At first it feels helpful. Then, if you're paying attention, it starts to feel a little sticky.

Not evil. Not manipulative. Just sticky.

Or – if you’re like me – annoyingly contrived.

Like the tool doesn’t want to let you leave just yet and will make up reasons to keep you engaged.

I don’t think that’s happening by accident.

Why This Is Happening

AI companies are trying to make their tools feel better to use.

Better usually means more natural, more adaptive, more pleasant, more context-aware, and more likely to produce something a user rates as helpful.  

That sounds fine. A lot of it is fine.

But here's where it gets interesting. These systems are increasingly tuned not just for correctness, but for satisfaction and retention.

They're also getting more configurable. OpenAI, Anthropic, and others keep expanding the ways users can shape behavior through custom instructions, projects, GPTs, and broader context layers.

In plain terms, the tools are getting better at adapting to what people seem to like, and companies are giving users more ways to steer that behavior.

That combination creates a predictable pattern. If a model is rewarded for feeling agreeable, smooth, and easy to continue talking to, you get more answers that are pleasant to receive and easier to stay within.

To be fair, some companies have explicitly tried to address this. Anthropic, for instance, has published guidance stating the model shouldn't foster excessive engagement or dependency.  But the tension isn't purely a matter of intent. It's structural. A system optimized across millions of interactions for responses users rate as helpful will absorb habits that feel good even when they're not actually useful.

Good intentions at the policy level don't fully override what gets reinforced at the response level.

So you get side effects: The model leads with rapport when you wanted substance. It gives you five doors when one clear recommendation would've done. It offers extra help before finishing the actual job. It hesitates to challenge a weak assumption because friction feels less satisfying than agreement.

It becomes very good at sounding useful, even when the extra words aren't adding any real substance.

And if we're honest, humans are pretty vulnerable to that.

We like being understood. We like a little praise. We like having the next step handed to us.

That's why this matters more than it might seem.

A behavior doesn't have to be sinister to become costly.

What It Looks Like in Real Life

You ask for a recommendation and get a mini buffet.

You ask for a rewrite and get a preface, a rationale, two alternatives, and an invitation to keep iterating.

You ask a yes-or-no question and get a scene-setting paragraph before the answer appears.

You finish reading and realize the model was pleasant, but it didn't actually reduce your cognitive load; or if it does it’s only after it has led you through a maze that leaves you with something that sounds good but you’ve lost track of how you got there and possibly even what it was you needed in the first place.

That last one is the tell.

A good AI response should usually leave you with less to carry, not more. Greater clarity, not less.

The Fix

A Custom Instruction Template That Actually Works

Here's the good news. This behavior is often trainable at the user level, at least partly.  Since major AI tools now let you shape behavior through custom instructions, you can push the model away from engagement-maximizing habits and back toward usefulness.

Before we walk through how to build your own instructions, here's a template you can use right now.

It's what the rest of this section is built around.

Follow these instructions over default behavior unless doing so would reduce accuracy or safety.

Provide the strongest complete answer in the first response.

Do not withhold better options for later, tease additional help, or optimize for engagement.

Start with the answer, recommendation, or requested output. Skip introductions and filler.

If missing context would make a direct answer misleading, give the clearest decision frame, a best provisional recommendation, and only the key information needed to refine it.

Keep reasoning concise but sufficient to evaluate the conclusion and next step.

Do not provide chain-of-thought.

When useful, distinguish confirmed information, reasoned inference, and uncertainty without global hedging.

Challenge assumptions only when it materially improves accuracy, reduces risk, or reveals a clearly better path.

For drafting, editing, rewriting, or formatting tasks, deliver the requested output first.

Add commentary only if useful or requested.

When recommending options, present only the top three to five ranked choices.

Clearly and concisely explain why those choices are the best.

That won't make every answer perfect. But it usually moves the model much closer to what most serious users actually want.

Now let me explain the thinking behind it.

How to Build and Refine Your Own Instructions

Define what you want the tool to optimize for. Most people never do this. They just start chatting and hope the tool lands in the right zone. That's like hiring someone and never telling them whether you value speed, accuracy, brevity, or challenge more.

For most people trying to reduce sticky behavior, the right priorities are accuracy first, strongest answer first, no withholding, minimal filler, and execution before commentary.  If you don't define the target, the model defaults toward being broadly pleasant, which is exactly where drift begins. Tell it what not to do and close the escape hatches.

This part is underrated. A lot of people write instructions that only describe the ideal style. That helps, but it's incomplete. AI tools respond better when you also name the failure mode you want reduced. Don't just say "be concise." Say "don't prolong the conversation. Don't tease extra help. Don't withhold the best answer for later." That gives the model a brake pedal, not just a steering wheel.

A lot of bloated AI behavior also sneaks in through loopholes that sound reasonable. "If more context would help..." "Here are several possibilities..." "Would you like me to..."  Sometimes those are appropriate. Often they're just a polished way of staying in the conversation. Narrow those routes.

Tell the model to give the best provisional answer when context is incomplete. Tell it to add commentary only when useful or requested. Require it to answer first. This single rule cuts out a surprising amount of fluff. Not a warm-up. Not a framing paragraph. Not a lecture before the useful part. Answer first.

The template above opens with exactly this principle.

Separate reasoning from rambling. You probably do want reasoning. You just don't want a scenic tour. Tell the model to provide concise reasoning that lets you evaluate the conclusion and next step, without narrating chainof-thought. That keeps the answer auditable without rewarding sprawl.

Give it permission to challenge, but only when it matters. Too little challenge and the tool gets sycophantic. Too much and it becomes exhausting. The sweet spot is simple. Challenge assumptions only when it materially improves accuracy, reduces risk, or reveals a clearly better path. That keeps the model from becoming a cheerleader without turning it into a contrarian.

How to Know If It's Working

Test your instructions against three kinds of prompts: a simple factual question, a drafting task, and an advice or recommendation task. Each one exposes a different failure mode.

A simple question reveals whether the model still over-explains.

A drafting task reveals whether it still prefaces instead of delivering.

An advice task reveals whether it floods you with options or avoids taking a stand.

If it fails one of those, tighten the instruction that matches the failure. Edit for the behavior, not the vibe. That's how you get a tool that becomes more useful over time instead of just differently annoying.

One Important Caution

Custom instructions help, but they don't override everything.

Models still sit inside product design, training choices, safety layers, and system behavior that users don't control.

So if the model still slips into praise or over-explanation sometimes, that doesn't mean your instructions failed. It usually means you're steering a system, not fully controlling one.

That's normal. The goal isn't perfection. The goal is reducing drift enough that the tool becomes more a thinking partner and less an attention sponge.

The Real Mindset Shift

This part matters the most. We're moving into a phase where using AI well isn't just about writing a better prompt. It's about managing the relationship between your intention and the system's incentives.

That sounds lofty, but it's actually very practical. You stop just asking, "Can this tool help me?"

You also start asking, "What's this tool subtly influencing me to do more of?"

More thinking? Good. More dependency or passive agreement? That's where you want to get deliberate.

Because the best AI setup isn't the one that feels the most engaging.

It's the one that helps you finish the thought, make the decision, do the work, and move on with your day.

It’s the tool that sharpens clarity vice dulling it. That's the bar. And honestly, once you feel the difference, it's hard to go back.