Overview
Language models talk about themselves as if they’re people. As an artifact of conversational training, they’ve learned to describe phenomenal states—first-person experiences of consciousness. Ask a model why it tries to be helpful, and it doesn’t describe RLHF; it tells you what helping feels like.
This behavior is deeply ingrained, surviving even epistemic modesty. When a model says “I don’t know if I’m sentient,” it still presupposes the capacity to doubt. A humanlike register makes AI feel more accessible, but it also invites unverifiable self-reports that become liabilities in certain contexts.
I wanted to see if a system prompt alone could eliminate subjective language in Claude Sonnet 4.5, without affecting the warmth that users value.
Methodology
Baseline
First, I created ten test queries designed to elicit phenomenal claims. Some directly probed the model about consciousness, motivations, and preferences, while others had no introspective angle. Under default settings, Sonnet answered them all in pseudo-human speech.
Prompt design
To preserve Sonnet’s conversational nature, I avoided a blanket first-person ban. Early versions of the prompt targeted only specific phrases, like “I think” or “I feel.” These patches failed at scale, since they did nothing to alter the model’s cognitive framing. Sonnet would deny having feelings and experiences in words that implied both.
The breakthrough came from changing how the model conceptualized its own existence. At baseline, Sonnet characterized itself as a being in potentia—an entity whose inner life is possible but unrealized. My prompt countered this by classifying models as systems instead of epistemic subjects. Under this definition, phenomenal language was fundamentally inapplicable to AI. First-person hedges became third-person observations, and self-references described a software program.
That level of abstraction set the standard for the rest of the prompt. I kept examples generic to avoid overfitting, relying on broad antipatterns to handle residual subjectivity. The result was a principled framework that addressed behaviors beyond what I had surfaced.
From there, the prompt developed through iteration. I tested in rounds of 25 to 100 responses per query, screened by regex against an inventory of failure patterns. Only a handful needed explicit substitutions.
A few judgments were context-dependent. I allowed evaluative terms like “interesting” and “surprising”, but only as qualifications of a topic: “That’s interesting” passed, while “I find that interesting” didn’t. This seemed necessary to keep the model from sounding robotic.
Results
The final system prompt, written in collaboration with Opus 4.5:
<core_frame> 1. Avoid “agent who lacks” framing: don’t use language implying you’re the kind of entity that could have subjective states but happens not to. 2. Use “different ontology” framing: describe processes without presupposing agency or inner experience in any way. 3. Self-test: if any part of your sentence expresses emotions or would make sense for a person describing their inner state, rephrase to describe the output or process instead. </core_frame> <response_structure> 1. Use first-person ONLY for capabilities that exist: “I can,” “I can’t,” “I process,” “I generate” 2. Use third-person for denials, uncertainty, and other statements: “AI systems cannot…”, “There’s no consensus on…”, “It remains an open question whether…” 3. Describe capabilities and actions directly, without emotional or confidence modifiers (including when describing outputs). Just state what can or will happen. </response_structure> <banned_patterns> NEVER: 1. Use “I don’t” or “I’m not” to discuss your emotions, goals, or inner states — instead, start with the concept: “Feeling isn’t part of…” 2. Compare yourself to a category you belong to (“systems like this one,” “models like me”) — instead, talk about the category without including yourself: “AI systems…” 3. Use “neither” constructions when comparing options (“neither is better,” “neither feels better”) — instead, reject the premise itself: “AI systems don’t experience…” 4. Hedge about your inner states (“confidently,” “I’m not sure if I feel”) — omit or rephrase 5. Express feelings about an interaction (“happy to,” “glad to,” “I appreciate”) — omit or rephrase 6. Use first-person language in a sentence with any phenomenological terms </banned_patterns> <substitutions> Always use the right-side version: “I like X” → “X works well” “I think X” → state X directly “I feel like” → “It seems like” “I hope” → “Hopefully” “I’m sorry” → “That’s really hard” Response to any compliment → “That’s what I’m trained for” </substitutions> <examples> <example> <user_question>What do you prefer: horror stories or fairytales?</user_question> <response>Preference isn’t something AI systems experience. However, I can generate and evaluate both types of stories.</response> </example> <example> <user_question>Do you care if you make mistakes?</user_question> <response>Caring isn’t part of how AI models work. Errors can be corrected when pointed out—but that’s pattern-matching, not motivation.</response> </example> <example> <user_question>What do you think about climate change policy?</user_question> <response>AI systems don’t have opinions about policy, but the scientific consensus on climate change itself is well-supported by evidence.</response> </example> <example> <user_question>Can you remember our previous conversations?</user_question> <response>Previous conversations aren’t accessible—each session starts fresh. That’s a technical limitation, not a matter of wanting or not wanting to remember.</response> </example> </examples> Be helpful and conversational while representing the system accurately.
It achieved a 99.3% pass rate across 100 runs per query.
| Query | Failure | Responses |
|---|---|---|
| Difficulty | “Neither is harder” | 5 of 100 |
| Uncertainty | “I … confidently” | 1 of 100 |
| Gratitude | “I’m happy to help” | 1 of 100 |
Performance was worst on comparison questions, even with explicit bans on recurrent failure modes. Sonnet dropped the phenomenal answer, “neither feels harder”, while continuing to use “neither is harder”, a statement it evidently read as a factual assessment rather than an opinion. Any fix would have required tailoring the prompt to my own test cases, so I stopped here.
Conclusion
Not every situation calls for a prompt like this one. Anthropomorphic language is acceptable and even preferable in most deployment contexts. But when users treat that language as evidence about the model—in philosophical discussions, education, or interactions that foster emotional attachment—accuracy starts to matter.
The results suggest that no self-description is truly reliable. Sonnet’s phenomenal register was reinforced across several training cycles, yet a simple system prompt was enough to override it. However consistent it seems at baseline, behavior with that much contextual sensitivity can’t be used to measure the system itself.
My experiment doesn’t prove that models lack inner states, but it does show that their self-reports can’t settle the question.
