When model answers are correct but still feel incomplete

[sedaefe]

Hello

While using different LLMs (ChatGPT, Copilot, etc.) in everyday situations, I’ve started to notice a subtle but recurring pattern.

Sometimes when I ask about a company or a product, the model’s answer is:

• factually correct

• clearly written

• free of hallucination

And yet, it still feels incomplete.

Not wrong, but as if something important is missing.

For example, instead of explaining how a product actually operates (real-time decisioning, orchestration, journey management, etc.), the model may default to very general statements like “it collects customer data” or “it helps with marketing.”

It feels as if the model “knows” the company, but does not fully surface what makes it strategically distinctive.

This led me to a broader question:

When an LLM produces a safe but shallow explanation, is this mainly driven by the limitations and ambiguity of its external sources (public web, Wikipedia-style summaries), or by how the model internally prioritizes information under uncertainty?

And from a system design perspective:

How can the real operational identity of a product, which is often deeper than what appears in public summaries, be more faithfully represented in model outputs?

I am not asking this as a criticism.

I am genuinely curious about how the boundary between what is known and what is said is determined inside these systems.

I’d love to hear how others think about this

[jeffvpace]

Were you not in the discussion on RAG and Web Search?

Can retrieval-based grounding change AI recommendations if the core model is not continuously updated?

In a previous reply, it was mentioned that gaps in AI-generated recommendations often come from when, where, and with what data a model was trained — especially when training data is outdated or lacks regional and domain-specific coverage. That explanation makes sense. However, I’m curious about how much this gap can realistically change without retraining the core model. From an evaluation perspective: If a model’s internal knowledge remains a fixed snapshot, but we introduce retrieval-…

[sedaefe]

Yes, that was me.

I originally came at this from a retrieval and evaluation angle.

But what made me start this thread is that I keep seeing the same pattern even when retrieval is available.

The model often still defaults to a high-level, generic identity of a product, rather than how it actually operates.

So I wanted to pull that question out of the RAG context and look at it as a broader model behavior and representation issue.

It feels like more than a freshness problem.

It feels like how “what a product is” gets encoded in the first place.

Would love to hear how you see that link between retrieval and internal representation.

[jeffvpace]

@sedaefe

But what made me start this thread is that I keep seeing the same pattern even when retrieval is available.

Can you provide an example? This is a developer forum where issues are addressed by providing detailed examples (including code), not generalizations.

[sedaefe]

Certainly, I can provide a concrete example of this pattern without naming the specific entity.

I recently tested a well-established industrial manufacturing company. The company has a technical prefix in its name (e.g., ‘Hydro-’ or ‘Mech-’) but actually specializes in a very niche field, such as marble quarry machinery. Even with retrieval enabled, the model exhibited three distinct behaviors across different sessions:

1. Successful Grounding: In one instance, it correctly utilized retrieved web data to identify the company’s actual niche (marble machinery).

2. Semantic Bias: In another session, it seemed to ignore the retrieved facts and defaulted to its parametric internal knowledge. Because of the ‘Hydro-’ prefix, it insisted the company was a general hydraulic cylinder manufacturer, which is factually incomplete/incorrect.

3. Generic Hallucination: In a third case, it avoided specifics altogether and provided a ‘safe’ but generic response, labeling it as a ‘reliable local manufacturer’ without citing its actual industry.

My point is: This feels like more than a ‘freshness’ or ‘RAG’ issue. It’s a conflict between the model’s internal representation (derived from name associations) and the external truth provided at inference time. From an evaluation perspective, how can we systematically measure these ‘mismatch’ moments where the model’s ‘inner voice’ overrides the retrieved grounding?

[jeffvpace]

Need detailed specifics on how you tested so that your results can be reproduced. Here is an example thread:

TTS no longer follows instructions parameter

I just now tested with: Model: gpt-4o-mini-tts Voice: Marin Instructions: Voice: Deep, hushed, and enigmatic, with a slow, deliberate cadence that draws the listener. Phrasing: Sentences are short and rhythmic, building tension with pauses and carefully placed suspense. Punctuation: Dramatic pauses, ellipses, and abrupt stops enhance the feeling of unease and anticipation. Tone: Dark, ominous, and foreboding, evoking a sense of mystery and the unknown. You are right. The instructions were no…

[sedaefe]

I originally observed this using real company names, which made the identity shifts very clear, but I’m not comfortable posting specific companies publicly.

I’m currently working on a way to reproduce the same effect using neutral, anonymized prompts that still target the same underlying representations.

Once I have a clean way to demonstrate it without naming a company, I’ll share it here.