GPT needs a truth-first toggle for technical workflows

1•PAdvisory•6mo 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•6mo 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•6mo 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__•6mo 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.

Decenterlized Login System for a Media Server Project?

The Era of Visual Studio Code

Workday project at Washington University hits $266M

Myna v2.0: contextual variants, more weights (and even supports APL)

Safari 26.2 Release Notes

Chrome Extension Manager

China's trade surplus tops record US$1T, defying trade war uncertainty

Purdue University Approves New AI Requirement for All Undergrads

The rise and fall of Subway: The $11 billion empire that crumbled

Human Agency: Protect your documents with hidden Anti AI directives

Sovereignty by Disruption: The Rise of Corporate Quasi-States

PaperTrails: Your Personal Research Library

Ancient lake that vanished 100k years ago returns to California

Want to sway an election? Here’s how much fake online accounts cost

Reproducibility Test-Time Training on Nearest Neighbors for LLMs

Australian teens were kicked off social media this week. Some are back already

RAMageddon is finally coming for your smartphones and laptops

More atmospheric rivers coming for flooded Washington and the West Coast

Before You Cite That Study

What happens when the coding becomes the least interesting part of the work

A brief natural history of misinformation

Enabling small language models to solve complex reasoning tasks

Benchmarking LLMs on whether they can play FizzBuzz

Why Twilio Segment Moved from Microservices Back to a Monolith

Faster Double-to-String Conversion

JD Vance: "You might try hiring Americans."

I Fed 24 Years of My Blog Posts to a Markov Model

The Rise of Computer Games, Part I: Adventure

Texas Space Boom Requires Lots of Lawyers in Boost for Firms

Microsoft Excel Conquered Corporate America