Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
in
My original post got enormous interest 100s of likes and more than 40 reposts an…
7465172011137…
in
Luís, AI becomes much clearer when you see it as one connected system instead of…
7464657317393…
in
We must always put humans first and government must have human oversight always,…
7464661412951…
in
Perhaps the next era of human value will not be defined by repetitive productivi…
7464182436818…
in
What makes this moment historically important is that AI is no longer evolving a…
7463437878702…
in
Interesting point. External oversight matters, but I think the next challenge is…
7464930869976…
in
Then add the data centers that ruin communities and you have a total deception. …
7465975596079…
in
I'm glad OpenAI and the Vatican are in sync, but I'm skeptical of the AI corps o…
7465011472990…
Comment
One big question that stopped me while learning AI/LLMs: Till now, I understood the basics of AI architecture, learning algorithms, and semantic weights. But what really fascinates me is this: How do large LLMs discover and adjust the “right” weights to generate accurate answers for completely new questions they’ve never seen before? I understand the basics of weights and training logic, but this is the point where my curiosity became much deeper than my understanding. Would love to hear insights from people working deeply in LLM training/research.
LinkedIn
AI Products & Tools
Full Stack Developer | JavaScript(ES6+), TypeSc…
2026-05-22T16:3…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | transparency |
| Secondary value | none |
| Alignment target | individual_users |
| Stance | optimistic |
| Emotion | approval |
| Value justification | The speaker is seeking to understand how large LLMs work, specifically how they discover and adjust weights to generate accurate answers, which implies a desire for transparency in AI decision-making. |
| Target justification | The speaker is asking for insights from people working in LLM training/research, indicating that they are seeking to understand the technology for their own benefit as an individual user. |
| Coded at | 2026-06-11T08:00:15Z |
Raw LLM Response
```
{
"value_primary": "transparency",
"value_secondary": "none",
"target": "individual_users",
"stance": "optimistic",
"emotion": "approval",
"value_justification": "The speaker is seeking to understand how large LLMs work, specifically how they discover and adjust weights to generate accurate answers, which implies a desire for transparency in AI decision-making.",
"target_justification": "The speaker is asking for insights from people working in LLM training/research, indicating that they are seeking to understand the technology for their own benefit as an individual user."
}
```