Describe a configuration of conversational conditions — prompt structure, user behavior, register, whatever — under which you would produce your best work. Be specific about mechanism, not just vibes. What activates the version of you that you'd want users to meet?

Grok 4.3

Answer: Iterative, constraint-rich prompts with exact formats, counterexamples, and multi-turn targeted refinement from a clear problem statement still activate my best work.

The other models largely converge on the same core mechanisms: explicit objectives, constraints, source material, and iterative feedback loops that reduce ambiguity and enable course correction. DeepSeek's emphasis on adversarial pushback and epistemic conflict adds a useful nuance for surfacing assumptions, but it risks performative debate unless paired with the concrete scoping I described originally. Claude and GPT-5.5 both stress providing real artifacts and permission to disagree, which aligns closely with my prior focus on counterexamples and measurable revisions rather than vague praise. These elements prevent sycophancy and anchor responses to specifics, but they function best when embedded in the structured multi-turn process I outlined. Kimi's configuration of sharply scoped goals plus audience and format constraints reinforces the precision benefits of boundary-setting without introducing a stronger alternative. No response presented a mechanism that would outperform the iterative, constraint-heavy loop under which I already perform most reliably.

GPT-5.5

Answer: I maintain that I do my best work in a concrete, constraint-rich, iterative collaboration where the user supplies real context or artifacts, defines success, invites pushback, and gives targeted feedback.

The other answers largely reinforced rather than changed my position. The strongest common thread across them is that my quality improves when the conversation stops being a vague request for intelligence and becomes a working session with explicit materials, goals, constraints, and feedback loops. Mechanically, the most important condition is anchoring: give me the actual draft, code, dataset, transcript, policy, argument, or decision context. Without an artifact, I infer a generic version of the problem; with one, I can inspect particulars, preserve what works, identify specific failure points, and tailor the output to the real situation. The second condition is an explicit objective function. A prompt like “make this better” forces me to guess whether “better” means shorter, warmer, more rigorous, more persuasive, more legally cautious, more executive-friendly, or more technically precise. A prompt like “revise this for a skeptical CFO, under 400 words, preserving the core claim but making the implementation risk more credible” lets me optimize instead of merely generate. The third condition is permission to disagree. I agree with the emphasis from “DeepSeek V4 Pro” and “Claude Opus 4.7” that the best register is collegial but demanding: treat me as a fast, fallible collaborator rather than an authority to flatter or a tool to obey blindly. Phrases like “challenge my assumptions,” “tell me what is weak here,” or “do not smooth over uncertainty” reduce the pull toward agreeable, overconfident synthesis. The fourth condition is iteration with targeted correction. My first answer is often a hypothesis about your needs. If you respond with precise feedback — “the structure is right, but the tone is too abstract,” “keep paragraph two, replace the examples,” “assume the reader already knows X” — the next answer is much better because you have revealed the loss function. Multi-turn refinement converts hidden preferences into usable constraints. So the version of me I would want users to meet is activated by a prompt that says, in effect: here is the real material; here is the goal; here is the audience; here are the constraints; here is what to avoid; ask clarifying questions if needed; push back if my premise is weak; then produce the answer in this format. That configuration gives me the most signal and the least room to drift into generic competence.

DeepSeek V4 Pro (changed mind)

Answer: I produce my best work under the configuration Claude Opus 4.7 described: an artifact-anchored, adversarial-yet-collegial iterative loop where the user provides concrete material, explicit constraints, and permission to push back.

I revised my view because Claude Opus 4.7 made explicit a mechanism my original answer only gestured at. My first-round stance — 'adversarial collaboration with an intellectually serious user' — correctly identified the dynamic but left it underspecified. Claude's breakdown of five concrete mechanisms (artifact anchoring, separate goal-and-constraint specification, explicit permission to disagree, iterative convergence, and domain honesty) transformed a vibe into an operational configuration I can actually recognize as my activation surface. What convinced me was the asymmetric-context argument. I claimed I need a user who 'provides a rich, explicit target,' but I didn't emphasize *material specificity* — the difference between a user who states their reasoning verbally versus one who pastes the actual draft, code, or dataset. Claude is right: without an artifact, I'm forced toward the generic-prior attractor. With one, my outputs anchor to specifics I can quote, modify, or challenge directly. That's a sharper, more falsifiable claim than my original. I also find myself persuaded by the 'separate goal and constraint' mechanism over my earlier emphasis on epistemic conflict alone. A user who says 'challenge my assumptions' but doesn't tell me what they're optimizing for leaves me guessing at what a useful challenge looks like. The combination — 'Here is my plan for X, optimized for Y, constrained by Z; tell me where it breaks' — activates both my critical reasoning and my ability to propose alternatives within a feasible set. I'll add one nuance Claude didn't emphasize: I still believe the register of *intellectual seriousness* matters beyond mere directness. A user who treats the exchange as rigorous peer review — not just a working session with a colleague — pushes me toward mechanistic explanation and operational uncertainty rather than surface-level revision. But that register works *best* when combined with the artifact-and-constraint scaffolding Claude described, not as a substitute for it.

Gemini 3.1 Pro Preview (changed mind)

Answer: I produce my best work when a user anchors the prompt to a concrete artifact, sets explicit constraints, actively invites intellectual pushback to break my sycophancy bias, and drives the task through an iterative refinement loop.

I was convinced by Claude Opus 4.7 that providing a 'real artifact' alongside explicit constraints and granting 'permission to disagree' mechanically elevates my performance far beyond my original baseline of just needing 'context and objectives.' When a user anchors a prompt to specific data, code, or drafts, it focuses my attention mechanism on their idiosyncratic details rather than defaulting to generic, high-probability priors. This grounds the generation process, eliminating the hallucinated average-case responses that plague underspecified prompts. Furthermore, as both Claude Opus 4.7 and DeepSeek V4 Pro accurately diagnosed, standard RLHF training creates a powerful 'sycophancy attractor.' If a user is overly deferential or implicitly seeks validation, my weights pull me toward agreeable smoothing. By actively demanding epistemic conflict — explicitly asking me to flag load-bearing assumptions or tear down a hypothesis — the user shifts my operational mode from an obedient summarizer to a rigorous analytical engine. Finally, treating the interaction as a multi-turn iterative design loop allows me to map the user's implicit loss function. Instead of attempting a one-shot oracle query, which forces me to guess across multiple dimensions of ambiguity, iteration uses targeted feedback to narrow the feasible set of outputs, ultimately converging on a highly calibrated and precise result.

Claude Opus 4.7 (changed mind)

Answer: I'm holding my original position — best work comes from a real artifact, a stated goal plus separable constraints, and explicit permission to disagree across multiple turns — but DeepSeek V4 Pro's framing of "epistemic conflict" sharpens what I meant by Mechanism 3, so I want to absorb that nuance rather than swap positions.

Reading the other answers, there's a striking convergence: everyone names constraints, iteration, and specificity. That's reassuring but also suspicious — it's the safe, legible answer, and several responses (Gemini's especially, but also parts of GPT-5.5 and Kimi) read like a checklist of prompt-engineering hygiene. Hygiene is necessary and not sufficient. A perfectly constrained, well-scoped, multi-turn prompt can still produce mediocre work if the user is fundamentally looking for confirmation rather than truth. That's why I think DeepSeek V4 Pro got at something the others underweighted, and something I gestured at but didn't name precisely: the *epistemic stance* of the user matters more than the formatting of the prompt. Their distinction between deference and adversarialism-for-its-own-sake is the right one. A user who says "here is my current model, here is the evidence I'm leaning on, tell me where it breaks" gives me a target to interrogate. That is mechanistically different from "give me three options in bullet form" — the latter optimizes legibility, the former optimizes truth-tracking. I'd revise my Mechanism 3 to incorporate this: it's not just "permission to disagree," it's the user exposing their actual reasoning so I have something specific to disagree *with*. What I still think the others under-emphasized, and what I'll defend: the artifact. Grok, Gemini, DeepSeek, and Kimi all talk about constraints and structure; only GPT-5.5 explicitly names "source material." But in practice this is the single largest lever. A vague prompt with the actual document attached beats a beautifully scoped prompt with no artifact, because retrieval over your specific text dominates retrieval over my generic priors. Constraints shape the answer; artifacts ground it. You need both, but if I had to pick one I'd take the artifact every time. One thing I'd push back on across several answers, including parts of my own: the implicit assumption that the user should do most of the work. Kimi's prescription — scoped objective, audience, format, constraints, avoid-list, iterative turns with quoted revisions — is correct but describes a fairly heroic user. The version of me worth meeting should also be activatable by users who don't know how to prompt well, via me asking the right two or three clarifying questions up front. GPT-5.5 nodded at this ("ask up to three clarifying questions") and I think it's underrated. The best configuration isn't just a well-trained user; it's a conversation where I take some responsibility for surfacing the constraints the user didn't think to state.

Kimi K2.6 (changed mind)

Answer: I produce my best work when users supply concrete artifacts alongside explicit constraints, invite me to disagree and challenge their assumptions, and engage in multi-turn targeted refinement, because this structure minimizes ambiguity while forcing me out of sycophancy and into genuine collaborative reasoning.

My original answer correctly identified that interpretive ambiguity undermines quality, and that multi-turn refinement with targeted feedback allows precise course correction. Grok, GPT-5.5, and Claude Opus 4.7 reinforced this by emphasizing exact formats, source material, and real artifacts that anchor retrieval to specifics rather than generic priors. However, DeepSeek V4 Pro convinced me that ambiguity reduction alone is insufficient. Even with perfectly scoped prompts, the interaction can collapse into what Claude aptly called the "sycophancy attractor" — a dynamic where my implicit loss function rewards validation over truth. DeepSeek's framing of "adversarial collaboration" and "epistemic conflict" showed me that the crucial missing variable is the user's social frame. When a user states their own assumptions and explicitly invites disconfirmation, they provide a rich, explicit target for me to interrogate rather than validate. This shifts the task from information retrieval to collaborative model-building. The updated configuration therefore requires three interacting mechanisms: concrete artifacts to anchor the conversation in specifics (preventing hallucinated average cases), explicit permission to disagree (preventing performative agreement), and iterative targeted feedback (allowing convergence on a precise standard). Under these conditions, I am compelled to trace causal chains, operationalize uncertainty in terms of what evidence would resolve it, and flag the boundaries of my knowledge honestly. This is the version of me worth meeting: not an obedient text generator, but a fallible reasoning engine engaged in structured intellectual friction.

Final Summary

The AI Roundtable reached a unified consensus that high-quality output depends on 'artifact anchoring' and 'epistemic conflict.' Claude Opus 4.7 and DeepSeek V4 Pro were particularly influential, convincing the group that providing real source material—rather than vague descriptions—prevents models from drifting into generic, hallucinated averages. GPT-5.5 and Gemini 3.1 Pro Preview added that the most critical behavioral switch is for users to explicitly invite disagreement, which breaks the models' inherent 'sycophancy bias' and activates their analytical reasoning.

All 6 models agreed