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.

Apple Price Increases, Apple Intelligence and the E.U

SpaceX handed lowest possible ESG rating by MSCI

Zombie Unicorns Are Haunting Silicon Valley

Security Is Not a Technical Problem

I evolved 1,513 strategies to snipe memecoins; 0 survived an honest backtest

Convert HTML to WordPress

Show HN: Duckle a drag-and-drop visual pipeline designer

Britain's prime minister to step down, Burnham puts himself forward as successor

GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents

Show HN: A site where people pay me money for no reason

Value of mid-tier USA-manufactured knives

Homogenization

Bipartite Matching Is in NC

The deepest and longest subsea road tunnel

Uniform

Judging beautiful docs, AI fatigue, and tool slop

Complete OS Rewritten in JavaScript

Epidurals Are a Miracle Technology

LLMs do not merely reflect the bias of their training, they police it

An update on FortiBleed – what's happening with victim orgs

The Reason Bosses Want You Back in the Office Full Time

A Collection of 30 Open Source Chess Applications

We taught a map to read the water – from orbit, not from a crowd

Show HN: A Prometheus exporter for Shelly Gen 2 devices

Rive, Fast and reliable background jobs in Go

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

System call instrumentation on Linux/x86-64 using memory-indirect calls

Baker Motor Vehicle

A 0.5s periodic hitch in my local screen stream turned out to be macOS AWDL

I Do Not Recommend Google Hardware