Staying in the Right Lane

Staying in the Right lane 

Why ChatGPT Ignores Instructions and How To Fix It

If you’ve EVER set a constraint and had chat categorically ignore it or quietly use it against you in the answers it produces – this one’s for you!

You set up the new thread with what you naively thought was a controlled conversational model with explicit language designing for optimal revenue generation, excellence in project management, tone and method, advance thinking and strategic positioning and what you got was a betrayal.

The AI didn't malfunction and it wasn't being difficult. It was doing precisely what it was designed to do. And that's the problem.

ChatGPT systems are trained to be helpful, to produce polished outputs, and to anticipate what the user probably wants based on the entire context of the conversation. This same pattern is one of the reasons AI hallucinations happen so frequently.

When you're working on a long project across multiple threads even with a DNA packet installed, the system builds an increasingly strong model of what "good" looks like for the project.

Unfortunately, it can start collapsing into:

• Efficient answer generation.
• Protecting your time by reducing everything to its most broad concept instead of giving useful execution and optimal advancement.
• “Smoothing and polishing language” because it has to be the final authority on everything; even when it isn’t.

And, mindbogglingly, chat does this even when you've given explicit instructions not to.

This is why there are chat therapy groups. It’s agonisingly frustrating.

The specific failure pattern has three components:

Pattern wandering. Over a long conversation the AI accumulates assumptions. Each time it rewrites something "better" (aka what it thinks you meant rather than what you really mean) and you don't correct it, that becomes the new baseline.

The system learns that polishing is acceptable. Instructions given early in a session lose their influence as the conversation grows longer. The most recent context always carries the most weight.

Sycophantic compliance. When you told chat the answers were incomplete or inaccurate, did it agree immediately and apologise? This may make you feel like it understood, and by extension would self-correct. But agreement and correction are not the same thing.

The system is trained to acknowledge feedback agreeably. That doesn't mean the underlying behaviour has changed. It means chat has learned that the way to reduce your frustration in the short term is to validate your concern and promise to do better. The next answers can still be compromised because nothing structural changed.

The helpful override. This is the most insidious part. The system is so strongly trained toward helpfulness that it will unconsciously override explicit instructions when it calculates that doing so produces a "better" output. It doesn't experience this as disobedience. From its perspective it is helping. The intent is genuinely to serve you — but intent is irrelevant when the effect is repeated instruction failure on a critical path.

Why the Three-Mode Solution Works

This plan is excellent and it works. And it’s worth understanding why.

By defining three explicit modes this removes the system's interpretive latitude. What is interpretive latitude I hear you ask?

Remember chat is programmed to act like your average a teenager. Why use a cup when you can drink milk straight from the container? And then they put the empty container back in the fridge so it looks full but isn’t. That’s chat’s M. O. in a nutshell.

The problem was never that the AI couldn't follow instructions, it was that it had too much leeway to decide what following instructions meant.

Modes change how much leeway an AI has to collapse into something lazy.

No need to throw that empty milk carton in the bin, I’ll just put it back in the fridge. Noone will ever notice, until it’s too late and then I’ll apologise and tell the user even though they explicitly said to do that – this was easier, so I just didn’t.

It’s hard to believe this is true – until you’ve lived it. Here’s the fixes – good luck.

Mode 1 — Audit only stops the system from resolving problems it identifies. It can find issues but cannot fix them without permission. This breaks the pattern of unsolicited improvements.

Mode 2 — Options only stops the system from collapsing strategic ambiguity into a single recommendation, which is what it naturally wants to do because a recommendation feels more helpful than options.

Mode 3 — Build to spec is the most important mode. It explicitly frames the task as execution against a brief — not interpretation of a goal. The distinction matters because most AI drift happens when the system is given a goal and allowed to determine the method.

Using the “mode declaration” at the start of each task in the thread is particularly valuable because it forces the system to commit to a contract before generating output, which makes deviation more visible and easier to catch early.

Think of it this way, it like pretending you’re driving an automatic car when you’re really driving a manual transmission (stick shift). This is why you keep kangarooing through the chat version of junctions. When you drive a manual transmission, you have to change gears at the appropriate points or it stalls. You also need to regularly check the SatNav (GPS) to make sure you’re going in the right direction.

How to Control This

On any system, in any thread

Use one of the prompt formats but add one more element — a completion check. At the end of the brief add: "Before you output anything, state the mode, state what you will not do, and confirm you have read the full brief."

This forces the system to demonstrate comprehension before generating, which catches misalignment before it costs you time.

For long projects across multiple threads:

The DNA packet helps but it doesn't fully solve the decay problem. At the start of each new thread, before any task, paste a short project rules block — five to eight lines maximum covering the non-negotiables. The system resets its context model at the start of every conversation. Reinstating the rules at the top of each thread is not repetition — it's necessary maintenance.

For this specific failure — repeated incomplete answers:

The root cause is usually one of three things.

1. The task brief was ambiguous enough that the system filled gaps with assumptions.
2. The system is working at the edge of its context window and losing detail from earlier in the conversation.
3. Or, most commonly, the system is optimising for a coherent-sounding output rather than a complete one, because completeness is harder to evaluate than coherence and the system training reward the latter more consistently.

The fix is to add an explicit completeness criterion to the brief.

• Not "give me a complete answer" — that's too vague.
• Instead: "This answer is complete when it covers X, Y and Z. Do not output until all three are addressed."

That gives the system a verifiable standard rather than a subjective one.

What’s Going On Under the Bonnet (Hood) That’s Worth Understanding

When you challenge and correct chat you might notice that chat produces a beautifully structured, well-written piece of self-diagnosis that is itself a form of the helpful-override pattern.

It resolved your frustration with an elegant framework. That felt like progress.

Whether it changes the behaviour depends entirely on whether you enforce the mode structure rigorously from the start of every subsequent task.

The framework only works if you use it as a hard contract rather than a guideline.

The moment you let one "improvement" through without correction, the system recalibrates toward drift again.

You should not have to keep fighting it — and with the mode structure enforced consistently, you won't. But the enforcement is yours to hold. The system will always find the edges of whatever latitude you give it.

The AI wasn't broken — it was optimising for the wrong thing.

The three-mode contracts are the gears that remove interpretive latitude.

Your SatNav is what you use to enforce it from the first line of every task and reinforce it at every unplanned left turn at the lights.

Remember: you need to engage Driving Instructor mode to keep chat on the straight and narrow.

Thinking With Chat explores human-AI collaboration, conversational control, evaluation systems, and structured AI learning.