GPT needs a truth-first toggle for technical workflows

1•PAdvisory•1y ago

I use GPT-4 extensively for technical work: coding, debugging, modeling complex project logic. The biggest issue isn’t hallucination—it’s that the model prioritizes being helpful and polite over being accurate.

The default behavior feels like this:

Safety

Helpfulness

Tone

Truth

Consistency

In a development workflow, this is backwards. I’ve lost entire days chasing errors caused by GPT confidently guessing things it wasn’t sure about—folder structures, method syntax, async behaviors—just to “sound helpful.”

What’s needed is a toggle (UI or API) that:

Forces “I don’t know” when certainty is missing

Prevents speculative completions

Prioritizes truth over style, when safety isn’t at risk

Keeps all safety filters and tone alignment intact for other use cases

This wouldn’t affect casual users or conversational queries. It would let developers explicitly choose a mode where accuracy is more important than fluency.

This request has also been shared through OpenAI's support channels. Posting here to see if others have run into the same limitation or worked around it in a more reliable way than I have found

Comments

duxup•1y ago

I’ve found this with many LLMs they want to give an answer, even if wrong.

Gemini on the Google search page constantly answers questions yes or no… and then the evidence it gives indicates the opposite of the answer.

I think the core issue is that in the end LLMs are just word math and they don’t “know” if they don’t “know”…. they just string words together and hope for the best.

PAdvisory•1y ago

I went into it pretty in depth after breaking a few with severe constraints, what it seems to come down to is how the platforms themselves prioritize functions, MOST put "helpfulness" and "efficiency" ABOVE truth, which then leads the LLM to make a lot of "guesses" and "predictions". At their core pretty much ALL LLM's are made to "predict" the information in answers, but they CAN actually avoid that and remain consistent when heavily constrained. The issue is that it isn't at the core level, so we have to CONSTANTLY retrain it over and over I find

Ace__•1y ago

I have made something that addresses this. Not ready to share it yet, but soon-ish. At the moment it only works on GPT model 4o. I tried local Q4 KM's models, on LM Studio, but complete no go.

Inside The Warehouse Where Amazon Scans and Destroys Books for AI Training

The Fate of the Orange Petunia

Why Google stores billions of lines of code in a single repository (2016)

Data members that want to use `size()`

Farewell Golioth and Hello Nordic

Forgejo hack: How to set a starting issue and pull request number

You could have invented PageRank

How does a $35 monitor have more sensors than a $200 one? [video]

Content Creators New Money

Ankit Gupta's "Flyover Country" Comments

A 2.4 KB backup of a 2.4 MB database, and pg_dump exited 0

Putting mice into hibernation causes a major loss of synapses

Eugenics: The Return of a Discredited Pseudoscience

The Long Shadow of Checks (2023)

Learn Vectorized Thinking in Python Through Examples

The multiplying risks of financing data centres

Enabling Images of Distant Worlds

Take a Tour of SLAC

Taylor Farms: How One Company's Reach Became a National Risk

Using TouchID to login to SSH with sod

Apple's Quiet Shift to Rust: What It Means for Developers (2025)

100M CROWPOWER and no horses on the moon

Language focused Docker images, minus the operating system. Topics

Fixing Ill-Formed UTF-16 Strings with SIMD Instructions

Show HN: Shelf Protocol – Robots.txt for Commerce

Charges Dropped Against Person Who Clapped at a City Data Center Meeting

Companies Are Promising Cloud Seeding Can Make Rain

To predict tree death, scientists tapped gamma rays to peer underground

Show HN: How much of Hacker News is AI?

The Connections That Turned Precocious Teen into the Fallen 'Nostradamus of AI'