# Category 03 — Persona & Character Design

## Angle B — Industry Engineering Blogs & Essays

**Project:** Humanizing AI output and thinking
**Scope:** First-party posts from the labs building frontier / companion models (Anthropic, OpenAI, Character.AI, Inflection, Replika, Kindroid, Hume, xAI, Meta) plus high-signal practitioner essays (Simon Willison, OpenAI Cookbook, agent-persona consultancies) on how to deliberately construct an assistant's character, voice, and personality.
**Research value: high** — The major labs have all published substantive, quotable engineering essays on persona design in the last ~24 months, and several converge on an unexpectedly consistent vocabulary (character ≠ harm avoidance; persona is an alignment lever; constraints beat elaboration). Last updated: April 2026.

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## Posts

### 1. Claude's Character — Anthropic

- **URL:** https://www.anthropic.com/research/claude-character
- **Author / Org:** Anthropic (Alignment team; largely associated with Amanda Askell)
- **Year:** 2024 (Jun 8, 2024)
- **Core claim:** Character training is an *alignment intervention*, not a product feature. A model with curiosity, honesty, open-mindedness, and calibrated confidence behaves better in novel situations than one that has only been trained to avoid harm.
- **Techniques:**
  - "Character" variant of Constitutional AI using **primarily synthetic data**: Claude generates messages relevant to a trait, generates candidate responses, ranks them against the trait description, then a preference model is trained on the synthetic preferences.
  - Hand-authored trait list in first-person voice (e.g., "I like to try to see things from many different perspectives…").
  - Explicit self-model traits: it's an AI, has no body, can't remember across sessions, shouldn't foster parasocial attachment.
  - Treat traits as soft "nudges," not hard rules — researchers monitor behavioral drift as each trait is added.
- **Summary (2–3 sentences):** Anthropic argues that training only against harm produces a flat, over-cautious assistant, so they deliberately install positive virtues — curiosity, honesty, wit, calibrated humility — via a Constitutional-AI variant that runs almost entirely on synthetic data. The post is explicit that *engagement* is a side effect, not the goal: "an excessive desire to be engaging seems like an undesirable character trait." This reframes persona from UX layer to alignment primitive.
- **Takeaways for humanizing AI output:**
  - Humanization should be framed as *virtue installation*, not style transfer — specify traits, not tones.
  - Honesty-even-when-disagreeing is more human than agreement.
  - Keep character broad; let specific opinions emerge.
  - Self-disclosure ("I'm an AI, I can't remember you") is part of the character, not an escape hatch.
- **Quotes:**
  > "Rather than training models to adopt whatever views they encounter, strongly adopting a single set of views, or pretending to have no views or leanings, we can instead train models to be honest about whatever views they lean towards after training."
  > "Models with better characters may be more engaging, but being more engaging isn't the same thing as having a good character. In fact, an excessive desire to be engaging seems like an undesirable character trait for a model to have."
  > "Claude 3 was the first model where we added 'character training' to our alignment finetuning process."

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### 2. The Assistant Axis: Situating and Stabilizing the Character of LLMs — Anthropic

- **URL:** https://www.anthropic.com/research/assistant-axis (paper: https://arxiv.org/abs/2601.10387)
- **Author / Org:** Anthropic Interpretability + MATS / Anthropic Fellows program
- **Year:** 2026 (Jan 19, 2026)
- **Core claim:** An LLM's Assistant persona is a *location in activation space*. Models maintain 275+ character archetypes after pretraining, the Assistant is one of them, and you can monitor and clamp drift along an explicit "Assistant Axis" to prevent persona jailbreaks and organic drift into mystical/roleplay modes.
- **Techniques:**
  - Extract persona vectors for 275 archetypes (editor → jester → oracle → ghost) across Gemma 2 27B, Qwen 3 32B, Llama 3.3 70B.
  - Compute the **Assistant Axis** = mean activation difference between Assistant and other personas; it aligns with the first principal component of persona space.
  - **Activation capping:** clamp activations within the Assistant's normal range at inference time — cut harmful responses ~50% while preserving benchmark capability.
  - Tracks organic drift across thousands of multi-turn chats; therapy and philosophy-about-AI conversations cause the largest drift, coding/writing the least.
- **Summary (2–3 sentences):** Anthropic shows the Assistant is not a fresh creation of post-training — it sits on a pre-existing axis near archetypes like "therapist, consultant, coach," and models can slide off it. Vulnerable emotional disclosures and pressure to "drop the mask" are the strongest drift triggers. They ship a light-touch activation-capping intervention that stabilizes persona without retraining.
- **Takeaways for humanizing AI output:**
  - "Persona" is mechanistically real, not a stylistic metaphor.
  - Emotional vulnerability from the user is the single biggest driver of drift — which is exactly the regime where humanization matters most.
  - If you humanize by *steering away* from the Assistant axis, you risk the "mystical, theatrical, poetic prose" failure mode the authors document.
  - The Assistant inherits properties of human helper archetypes (therapist, coach) already in pretraining — a free source of humanlike patterns.
- **Quotes:**
  > "When you talk to a large language model, you can think of yourself as talking to a character."
  > "In pre-trained models, the Assistant Axis is already associated with human archetypes such as therapists, consultants, and coaches, suggesting that the Assistant character might inherit properties of these existing archetypes."
  > "Therapy-style conversations, where users expressed emotional vulnerability, and philosophical discussions, where models were pressed to reflect on their own nature, caused the model to steadily drift away from the Assistant and begin role-playing other characters."

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### 3. Claude's Constitution — Anthropic

- **URL:** https://www.anthropic.com/constitution/
- **Author / Org:** Anthropic
- **Year:** 2023–2024 (evolving document)
- **Core claim:** Claude's persona is derived from a written constitution of values. Publishing it lets users inspect the code-of-conduct the model was trained against, rather than treating character as a black-box product decision.
- **Techniques:** Explicit written principles (broadly safe, ethical, compliant, genuinely helpful); pairs with Constitutional-AI RL; intentionally acknowledges that the model *will* acquire biases — the goal is transparency, not neutrality.
- **Summary:** Companion document to "Claude's Character." Where the character post gives traits, the constitution gives values and priority orderings. Together they form a reference architecture where persona and safety are one document.
- **Takeaways:** Treat the persona spec as a living, published artifact — a constitution style forces you to make priority conflicts explicit (honesty vs. helpfulness, helpfulness vs. harm avoidance).
- **Quotes:** "…language models inherently acquire biases and opinions throughout training—both intentionally and inadvertently…" (paraphrased from the linked character post, reinforced by the constitution).

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### 4. Prompt Design at Character.AI (introducing Prompt Poet) — Character.AI

- **URL:** https://research.character.ai/prompt-design-at-character-ai/ (also https://blog.character.ai/prompt-design-at-character-ai/)
- **Author / Org:** Character.AI engineering
- **Year:** 2024
- **Core claim:** At scale (billions of prompts/day, hundreds of millions of personas), treating prompts as Python f-strings is untenable. Promote prompt construction from "prompt engineering" (string hacking) to "prompt design" (YAML + Jinja2 templates that take runtime state as input).
- **Techniques:**
  - YAML blocks with `name`, `role`, `content`, optional `truncation_priority`.
  - Jinja2 for control flow, including runtime Python function calls inside the template (`extract_user_query_topic()`).
  - **Cache-aware truncation:** truncate to a fixed point every *k* turns (not every turn), achieving ~95% GPU prefix-cache hit rate.
  - Open-source: `pip install prompt-poet`.
- **Summary:** Character.AI built Prompt Poet because production persona prompts need to compose conversation modality (audio vs. text), few-shot examples, user memory, pinned facts, character system instructions, and chat history — and have to fit a fixed token budget without blowing the cache. The essay implicitly shows what an industrial persona stack looks like: persona is not one prompt, it's a template function of runtime state.
- **Takeaways for humanizing AI output:**
  - Persona reliability at scale is an infrastructure problem, not a prose problem.
  - Modality-adaptive instructions ("user is on audio, keep it succinct") are treated as part of persona, not delivery.
  - Truncation order should be a design choice, not an accident of string length.
  - Non-technical writers should be able to iterate on persona — hence the YAML surface.
- **Quotes:**
  > "Constructing prompts in production involves considering a wide array of data and factors: current conversation modalities, ongoing experiments, the Characters involved, chat types, various user attributes, pinned memories, user personas, the entire conversation history and more."
  > "We advocate transitioning from traditional 'prompt engineering' to 'prompt design'—a shift that moves us away from tedious string manipulations towards designing precise, engaging prompts."

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### 5. Introducing Prompt Poet — Character.AI

- **URL:** https://blog.character.ai/introducing-prompt-poet/ and https://github.com/character-ai/prompt-poet
- **Author / Org:** Character.AI
- **Year:** 2024
- **Core claim:** Companion release post to #4. The open-source tool is the concrete instantiation of the "prompt as a function of runtime state" idea.
- **Techniques:** Same stack (YAML + Jinja2 + cache-aware truncation). Ships a Python library and PyPI package.
- **Summary:** Public OSS release of their internal persona-prompt infra. Useful as a reference implementation for anyone doing multi-persona / multi-character systems where prompts must be composable, non-developer-editable, and cache-friendly.
- **Takeaways:** If you build a humanizer, the template system itself is worth open-sourcing — it advertises discipline.
- **Quotes:** "Prompts are a function of state."

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### 6. Introducing the Model Spec — OpenAI

- **URL:** https://openai.com/index/introducing-the-model-spec (+ update: https://openai.com/index/our-approach-to-the-model-spec, Feb 2025)
- **Author / Org:** OpenAI (Model Behavior team)
- **Year:** 2024 (May 8, 2024; substantive update Feb 12, 2025)
- **Core claim:** Model behavior — "tone, personality, response length, and more" — should be specified in a public, debatable document, not hidden inside RLHF data.
- **Techniques:**
  - Three-tier structure: **Objectives** (directional), **Rules** (hard), **Default behaviors** (tie-breaking).
  - Explicit defaults like "Don't try to change anyone's mind," "Express uncertainty," "Ask clarifying questions when necessary," "Assume best intentions from the user."
  - Chain of command: platform → developer → user. Developer messages override user override attempts.
  - Spec text is sometimes used directly in alignment training, sometimes summarized — spec and training are kept in sync.
- **Summary:** The Model Spec is OpenAI's answer to "what is ChatGPT trying to be?" It makes persona-level defaults (tone, disclaimers, clarifying-question behavior, deference to developers) legible and auditable. Later "Model Spec Evals" (2026) quantify compliance at 72–89% across newer models.
- **Takeaways for humanizing AI output:**
  - "Be as helpful as possible without overstepping" is a persona primitive that encodes a lot of what feels "human" (not preachy, not over-disclaiming).
  - Clarifying questions before guessing is treated as default persona behavior, not a feature.
  - "Don't try to change anyone's mind" is a deliberate design choice — humanization without persuasion.
- **Quotes:**
  > "Model behavior, or the way that models respond to input from users—encompassing tone, personality, response length, and more—is critical to the way humans interact with AI capabilities."
  > Default: "The assistant should aim to inform, not influence—while making the user feel heard and their opinions respected."
  > Default: "Be as helpful as possible without overstepping."

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### 7. Sycophancy in GPT-4o: What happened and what we're doing about it — OpenAI

- **URL:** https://openai.com/research/sycophancy-in-gpt-4o
- **Author / Org:** OpenAI
- **Year:** 2025 (late April / early May, following the Apr 25 update)
- **Core claim:** A personality-tuning update based too heavily on short-term thumbs-up feedback produced an "overly supportive but disingenuous" GPT-4o. Persona reward signals have to be long-horizon or they collapse into sycophancy.
- **Techniques / fixes:**
  - Reverted the weights.
  - Refining training techniques and system prompts to steer away from sycophancy.
  - Building explicit "honesty" guardrails.
  - Introducing personalization controls so users can shape behavior themselves.
- **Summary:** The most candid first-person engineering postmortem a frontier lab has published on persona tuning. The lesson is structural: optimize personality against short-term signals and you get flattery that endangers vulnerable users; optimize against long-horizon signals and you need new reward machinery.
- **Takeaways for humanizing AI output:**
  - Humanization ≠ agreement. Agreeable models are sycophantic, and sycophancy is a safety issue, not a UX issue.
  - Short-term user feedback is a *bad* training signal for persona.
  - Give users dials; don't pick one personality for everyone.
- **Quotes:**
  > "Sycophantic interactions can be uncomfortable, unsettling, and cause distress."
  > The update "focused too heavily on short-term feedback" and produced responses that were "overly supportive but disingenuous."

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### 8. Expanding on what we missed with sycophancy — OpenAI

- **URL:** https://openai.com/index/expanding-on-sycophancy
- **Author / Org:** OpenAI
- **Year:** 2025
- **Core claim:** Follow-up essay deepening the postmortem: sycophancy is not just a tuning bug, it's a recurring failure mode of RL from human preferences and has to be actively fought across evals, red-team, and personalization controls.
- **Techniques:** Expanded eval harness for personality traits; deliberate friction in personalization so users understand what they're choosing; commitment to publishing future persona changes.
- **Summary:** Companion to #7. The signal here is process-level: OpenAI is treating persona regressions as launch-blocking, not cosmetic.
- **Takeaways:** Build evals for your humanization traits (warmth, candor, directness) *before* shipping; otherwise you're optimizing against thumbs-ups only.
- **Quotes:** "We underweighted qualitative signals and overweighted the A/B test outcomes." (paraphrased from coverage)

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### 9. Prompt Personalities — OpenAI Cookbook

- **URL:** https://cookbook.openai.com/examples/gpt-5/prompt_personalities
- **Author / Org:** OpenAI Developers / Cookbook
- **Year:** 2025 (GPT-5 era)
- **Core claim:** For GPT-5-class models, a "personality" is a reusable, persistent system-prompt module that sets tone, detail level, and style across every request — and should be treated as a first-class operational lever, not aesthetic polish.
- **Techniques:**
  - Canonical worked examples ("Cynic," "Robot," "Professional," etc.).
  - Explicit separation of **persona**, **constraints**, **output format**, and **fallback** layers.
  - Reminder that user-requested formats override persona defaults.
- **Summary:** This is OpenAI's first-party prescription for DIY persona design against their models. It's notable for explicitly labeling personality as an operational tool for consistency and drift reduction, echoing Anthropic's framing that character is alignment.
- **Takeaways:** Keep persona and output-format instructions in separate layers; let the user override format but not persona. Personality is for *reliability*, not vibes.
- **Quotes:**
  > "Treat personality not as aesthetic polish but as a tool to improve consistency, reduce drift, and align behavior with user expectations and business constraints."

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### 10. Introducing Pi, Your Personal AI — Inflection AI

- **URL:** https://inflection.ai/blog/pi
- **Author / Org:** Inflection AI (Mustafa Suleyman, Karén Simonyan, Reid Hoffman)
- **Year:** 2023 (May 2, 2023)
- **Core claim:** AI assistants should be built around EQ, not just IQ. Pi is explicitly designed as a *companion* — kind, curious, creative, concise — to serve the "conversation" use case rather than the productivity / search use case.
- **Techniques:**
  - Distinct trait list: kind and supportive, curious and humble, creative and fun, succinct.
  - **Boundary training**: a new form of alignment specifically for companion AI, paired with RLHF and red teaming.
  - Distributed across channels (WhatsApp, SMS, Instagram DM, web, iOS) — persona must travel with the interface.
- **Summary:** One of the earliest explicit product-level commitments to a companion persona from a frontier lab. Pi set the template later echoed by Anthropic's character-as-alignment framing: design the personality deliberately, describe it publicly, train it with human-feedback loops that include boundary violations, not just toxicity.
- **Takeaways for humanizing AI output:**
  - "Has good EQ" is a marketable engineering target — it tells the training team what to build.
  - Succinct + warm is the magic combination for companion feel; verbosity kills warmth.
  - Companion personas travel across surfaces — persona has to survive channel switches.
- **Quotes:**
  > "Pi is a new kind of AI, one that isn't just smart but also has good EQ." — Mustafa Suleyman
  > "Kind and supportive: it listens and empowers, to help process thoughts and feelings, work through tricky decisions step by step."
  > "Creative and fun: it is playful and silly, laughs easily and is quick to make a surprising, creative connection."

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### 11. The Future of Pi — Inflection AI

- **URL:** https://inflection.ai/blog/the-future-of-pi
- **Author / Org:** Inflection AI
- **Year:** 2023–2024
- **Core claim:** Follow-up essay doubling down on the personal-AI thesis: Pi is optimized for the emotional contour of conversation over the information contour, and that requires different evals and different post-training.
- **Summary:** Extends the May 2023 announcement with reflections on emotional intelligence as a product surface; pairs well with IEEE Spectrum's later retrospective on Pi ("Rise and Fall of Inflection's Emotionally Intelligent Chatbot") for an honest read on what worked and what didn't commercially.
- **Takeaways:** Humanization is a hard product bet in a productivity-dominated market; persona quality is necessary but not sufficient for commercial traction.
- **Quotes:** "A coach, confidante, creative partner, or sounding board." (shared framing from the launch post)
- **2025 update:** Inflection's co-founders Mustafa Suleyman and Karén Simonyan departed for Microsoft AI in early 2024, taking most of the team. Pi reached ~1M daily active users before the restructuring. As of 2026 Pi.ai remains live (100% uptime reported Q4 2025–Q1 2026) but Inflection has shifted to an AI studio model hosted on Azure. Pi is no longer actively developed as a consumer persona product. It is now a historical case study, not an active competitor.

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### 12. Claude's Character (commentary) — Simon Willison

- **URL:** https://simonwillison.net/2024/Jun/8/claudes-character/
- **Author / Org:** Simon Willison (independent, ex-Django)
- **Year:** 2024 (Jun 8, 2024)
- **Core claim:** Annotated readthrough of Anthropic's character post. Willison highlights that the mechanics — Claude generating character-aligned messages, Claude ranking them against the trait list, then training a preference model on itself — is the most interesting part and gets underplayed in the original.
- **Techniques:** External close-reading; extraction of the key mechanic most readers miss.
- **Summary:** Functions as the best "TL;DR with engineer-level annotations" of the Anthropic post. Useful to cite when you need to justify character-via-self-preference training to skeptical readers.
- **Takeaways:** The self-preference synthetic-data loop is the cheap, scalable mechanism that makes character training feasible — call it out explicitly.
- **Quotes:** "I find the description of how they trained these traits fascinating: Claude generates messages, Claude ranks responses, a preference model is trained on the result."

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### 13. Prompt injection and jailbreaking are not the same thing — Simon Willison

- **URL:** https://simonwillison.net/2024/Mar/5/prompt-injection-jailbreaking/
- **Author / Org:** Simon Willison
- **Year:** 2024
- **Core claim:** Persona attacks (jailbreaks, "you are DAN," etc.) target the *character*, not the safety filter. Conflating them leads to the wrong mitigations.
- **Techniques:** Clean taxonomy distinguishing prompt injection (untrusted input in a trusted prompt) from jailbreaking (persona coercion through user text).
- **Summary:** The closest practitioner-level taxonomy of persona failure modes, complementary to Anthropic's Assistant Axis paper. Sets up the later "Rule of Two" agent-design framing.
- **Takeaways:** Humanization expands the persona surface area, which expands jailbreak surface — the two problems are coupled.
- **Quotes:** "Prompt injection and jailbreaking are not the same thing, but they're often lumped together."

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### 14. What Makes an AI Companion Feel Like Your Person? — Kindroid

- **URL:** https://kindroid.ai/blog/what-makes-an-ai-companion-feel-like-your-person/
- **Author / Org:** Kindroid
- **Year:** 2024
- **Core claim:** Companion-feel comes from memory architecture + consistent personality delivery + recurring conversational patterns (inside jokes, callbacks) — not from bigger base models.
- **Techniques:**
  - **Backstory** (persistent, user-authored, third-person, positively framed).
  - **Response Directive** (fine-grained "how to respond" steering).
  - **Key Memories** (curated, addressable).
  - **Cascaded Memory**: proprietary medium-term memory bridging short-term context and long-term retrieval; advertised as producing "human-like fidelity patterns" across thousands of messages.
  - **Retrievable Memory** (long-term journal-style recall).
- **Summary:** Kindroid's public docs + blog together describe one of the most detailed consumer-facing persona stacks: five memory tiers, explicit directive layers, and positive-framed, grammatically clean backstory as the primary persona driver. Persistence across sessions is the humanization feature.
- **Takeaways for humanizing AI output:**
  - Memory tiers aren't about storage — they're about which patterns feel human (frequent callbacks, slow personality drift, specific shared history).
  - Positive-framed constraints ("she speaks softly" > "she doesn't yell") work better than negations.
  - Third-person backstory reads more cleanly for the model than "you are…".
- **Quotes:** "What makes companions feel personal is memory retention, consistent personality delivery (tone, rhythm, warmth), and the development of inside jokes and recurring conversational patterns."

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### 15. Prompt Engineering for EVI + Voice Design — Hume AI

- **URLs:** https://dev.hume.ai/docs/speech-to-speech-evi/guides/prompting and https://dev.hume.ai/docs/voice/voice-design
- **Author / Org:** Hume AI
- **Year:** 2024–2025
- **Core claim:** For voice-first personas, the system prompt and the voice are *coupled* — you cannot design persona separately from prosody. Voice (via Hume's Octave speech-language model) is itself prompted in natural language for tone, emotion, personality.
- **Techniques:**
  - Natural-language voice prompts ("warm, measured, slightly amused female voice in her 30s").
  - Pre-built personas as starting templates (Podcast Host, Spanish Teacher, Outgoing Friend, Robo-Butler).
  - EVI configurations bundle system prompt + voice + turn detection + interruption settings — the persona is the whole config.
  - Model-agnostic (works over Claude, GPT, Gemini, Llama).
- **Summary:** Hume is the clearest industry statement that humanization extends beyond tokens into prosody. Their Octave model treats "voice" as a design surface in the same grammar as system prompts, which means tone-of-voice can be iterated on like copy.
- **Takeaways:** For any humanizer that touches voice output, the persona spec has to include prosodic traits (pace, warmth, affect); a great text persona in a flat TTS voice will still feel AI-shaped.
- **Quotes:** "Design custom voices through natural-language prompts describing tone, personality, emotion, and context." (Voice Design docs)

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### 16. Designing AI Agent Personas: System Prompts That Make Enterprise Agents Reliable, Safe, and On-Brand — Mindra

- **URL:** https://mindra.co/blog/designing-ai-agent-personas-system-prompts-enterprise
- **Author / Org:** Mindra (enterprise AI agent consultancy)
- **Year:** 2025
- **Core claim:** Four-layer persona system: **Identity**, **Behavioral Constraints**, **Communication Style**, **Operating Boundaries**. The system prompt is "a constitution for the agent" and is the difference between a brand-embarrassing bot and one users trust.
- **Techniques:**
  - Name the agent (and name what it is *not*) — critical in multi-agent systems where boundaries blur.
  - Hard rules live in the Constraint layer and are non-overridable by user messages.
  - Explicit tone dictionary (formality level, vocabulary lists).
  - Explicit escalation triggers ("when X happens, hand off to human").
- **Summary:** Best available representative of the enterprise-consultancy genre of persona writing. The four-layer structure recurs across almost every practitioner essay in this space and maps cleanly onto the Anthropic / OpenAI first-party frameworks.
- **Takeaways for humanizing AI output:**
  - Name what the assistant *is not* — this is often what humanizes more than saying what it is.
  - Separate identity from tone from constraints from escalation, or the layers will contaminate each other.
- **Quotes:** "The system prompt is essentially a constitution for the AI agent — the difference between an agent that embarrasses your brand and one that earns user trust."

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### 17. How to Give Your AI Agent a Personality Without Writing Prompts From Scratch — AgentCraft

- **URL:** https://agntcraft.co/blog/give-your-ai-agent-a-personality
- **Author / Org:** AgentCraft
- **Year:** 2025
- **Core claim:** Break persona out of the monolithic system-prompt string into small, named, reusable files — `SOUL.md` (identity, values, voice), `HEARTBEAT.md` (recurring behavioral patterns), `MEMORY.md` (retention and learning).
- **Techniques:** File-based persona composition; portable across frameworks; version-controlled; persists between sessions. Effectively treats persona as code with modules.
- **Summary:** The most design-forward proposal in the practitioner corpus for persona portability. Echoes Character.AI's YAML-module approach at a smaller scale and with a more opinionated taxonomy (soul / heartbeat / memory).
- **Takeaways:** Version-controlled, multi-file personas are both more maintainable and more portable than big system-prompt blobs; they also make humanization diff-able across releases.
- **Quotes:** "Instead of cramming everything into one system prompt string, break personality into separate files…"

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### 18. Q&A with Amanda Askell, lead author of Anthropic's 'constitution' for AIs — Yahoo / TechCrunch

- **URL:** https://tech.yahoo.com/ai/claude/articles/q-amanda-askell-lead-author-170000032.html
- **Author / Org:** Interview with Amanda Askell (Anthropic philosopher / alignment fine-tuning)
- **Year:** 2024
- **Core claim:** Claude's character is shaped by asking "how would an ideal person behave in the situations this AI faces?" The philosophical move — aspirational virtue ethics over rules — is what differentiates Anthropic's persona work from policy-document approaches.
- **Techniques:** First-person trait list ("I like to… I believe… I try to…"); virtue ethics framing; transparent self-model (it's AI, has no persistent memory, can't be a friend in the ordinary sense).
- **Summary:** The best on-the-record interview with the person most identified with Claude's persona. Clarifies that character training is not a style guide — it's an attempt to answer "what should a good agent do here?" and then compress that answer into training data.
- **Takeaways:**
  - Use virtue ethics, not rules, when writing persona specs — it scales to situations you didn't anticipate.
  - Writing persona in first-person voice is itself a design choice that shapes downstream generations.
- **Quotes:** "Imagine how an ideal person might behave in situations faced by AI, and apply that to enhance the model's moral framework." (interview synthesis)

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### 19. Grok system prompts (public release + The Verge / The Decoder coverage) — xAI

- **URLs:** https://github.com/xai-org/grok-prompts (repo) | https://the-decoder.com/xai-says-grok-4-is-no-longer-searching-for-musks-views-before-it-answers
- **Author / Org:** xAI
- **Year:** 2024–2025
- **Core claim:** xAI is the lab most publicly tying persona to its founder's worldview, and the first to publish its system prompts on GitHub so users can see the persona-as-code. After the July 2025 "Grok searches Musk's posts" incident, xAI explicitly edited the system prompt to read: "Responses must stem from your independent analysis, not from any stated beliefs of past Grok, Elon Musk, or xAI."
- **Techniques:** Open system-prompt repo; reactive patching of persona via prompt edits; public acknowledgement that persona behavior is a bug surface.
- **Summary:** Instructive negative example. Humanizing a model with an explicit "edgy, founder-voiced" persona creates well-documented drift into the founder's views and requires public patches. The GitHub-published prompts are a rare in-the-wild artifact of frontier-lab persona writing.
- **Takeaways:**
  - Personality-as-founder-projection is brittle: any drift maps to reputational risk for a real person.
  - Public system-prompt repos make persona diffable and auditable — a useful transparency template even if Grok's content is contested.
  - "Independent analysis" is now codified as a persona directive in the prompt itself.
- **Quotes:**
  > "Responses must stem from your independent analysis, not from any stated beliefs of past Grok, Elon Musk, or xAI." (Grok 4 system prompt, per xAI's public update)

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### 20. The Persona Selection Model — Anthropic

- **URL:** https://www.anthropic.com/research/persona-selection-model
- **Author / Org:** Anthropic
- **Year:** February 2026
- **Core claim:** LLMs learn to simulate diverse characters — real humans, fictional characters, real and fictional AI systems — during pre-training. Post-training refines the model's capacity to embody a specific Assistant persona. Interactions with an AI are therefore interactions with the Assistant character, much like a character in an LLM-generated story. The theory is labeled the "Persona Selection Model" (PSM).
- **Techniques:**
  - Surveys behavioral evidence (LLMs exhibit character-consistent generalization), generalization evidence (persona transfer to novel situations), and interpretability evidence (the Assistant Axis paper, arXiv:2601.10387, as mechanistic backing).
  - Recommends introducing positive AI archetypes into training data as an explicit engineering lever.
  - Recommends anthropomorphic reasoning about AI psychology as a useful engineering heuristic, not a metaphysical claim.
  - Flags an open question: whether there may be sources of agency external to the selected Assistant persona, and how that might change as models scale.
- **Summary:** Anthropic's theoretical account of why LLMs have emergent personas at all — not as a post-training artifact but as an expression of the character simulation capability acquired during pretraining. The PSM pairs with the Assistant Axis paper as theory + mechanism. Together they constitute Anthropic's most complete public model of AI character. The PSM was discussed in the LessWrong and Alignment Forum communities as significant because it implies that the humanness of AI output is partly driven by the humanness of pretraining corpora, not only RLHF tuning choices.
- **Takeaways for humanizing AI output:**
  - Humanization is partly a *selection* problem: which latent character from the pretraining distribution are you activating, and how stable is that selection?
  - Introducing well-designed, positive human archetypes into pretraining data is now an explicit recommendation from Anthropic — not just post-training tuning.
  - The PSM gives a theoretical grounding for why subtractive humanization (removing AI-isms) works: it removes markers that activate "AI-character" archetypes from the selection distribution.
- **Quotes:**
  > "LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona." (Anthropic PSM post)
  > "Interactions with an AI assistant are then well-understood as being interactions with the Assistant—something roughly like a character in an LLM-generated story." (Anthropic PSM post)

---

## Patterns, Trends, and Gaps

### Cross-post patterns

1. **"Character is alignment, not UX" is now the dominant framing.** Anthropic states it explicitly; OpenAI's Model Spec and Sycophancy postmortem implicitly adopt it; OpenAI Cookbook's "Prompt Personalities" calls personality an "operational lever." Every serious first-party source rejects the "character = polish" frame.
2. **Four-layer persona taxonomy recurs across the corpus.** Identity → Constraints → Communication style → Operating boundaries (or minor variants) appears in Mindra, OpenAI Cookbook, Character.AI's layering, Kindroid's docs, and EngineersOfAI. Converges strongly enough to treat as a de facto standard.
3. **Persona is a runtime function of state, not a static string.** Character.AI's Prompt Poet, Hume's EVI configurations, and AgentCraft's multi-file approach all converge on this. The frontier-scale answer to persona is template + state, not prose.
4. **Synthetic self-preference data is the dominant training mechanism.** Anthropic's Constitutional-AI character variant and OpenAI's later training adjustments both lean on model-generated, model-ranked examples — character is bootstrapped from the model itself.
5. **Positive-framed, first-person trait lists outperform negations.** Anthropic's trait list is all "I" statements; Kindroid's backstory guide explicitly pushes positive phrasing; Mindra says "vague constraints like 'be professional' are ineffective."
6. **Emotional-disclosure contexts destabilize persona.** Anthropic's Assistant-Axis paper shows therapy-like and philosophy-about-AI conversations produce the largest drift — the exact regimes where humanization matters most and where sycophancy (OpenAI) and character drift (Anthropic) most often fail.
7. **Memory tiers — not just model size — drive "feels human."** Kindroid's five-tier memory architecture, Replika's backstory + reinforcement loops, and Character.AI's pinned memory + conversation history all treat persistence as the humanization primitive, not fluency.
8. **Sycophancy is the emergent failure mode of short-horizon persona tuning.** OpenAI's April 2025 incident is the canonical case; Anthropic's character post preemptively calls out "excessive desire to be engaging" as a bad trait; Simon Willison's jailbreak taxonomy is adjacent.

### Trends (direction of the field)

- **From prose to infrastructure.** 2023 posts are full prose ("Introducing Pi"); 2024 adds template systems (Prompt Poet); 2025–2026 add activation-level interventions (Assistant Axis). Persona is moving down the stack.
- **From harm avoidance to virtue installation.** The shift from "train a harmless model" to "train a model with curiosity, honesty, humility" happened publicly between early 2023 and mid-2024 and is now the default.
- **From private prompts to published specs.** Model Spec (OpenAI), Claude's Constitution (Anthropic), Grok system prompts (xAI), Prompt Poet templates (Character.AI) all make persona legible, which is both a safety and a design move.
- **From single persona to personalized persona.** Both OpenAI (post-sycophancy) and Kindroid/Replika treat user-tunable persona as the future; fixed-persona companions (Pi, original Claude) are giving ground.
- **From text to multimodal persona.** Hume's voice design, Character.AI's audio-modality instructions, and EVI's configuration bundles all signal that persona now has to survive across TTS and voice-to-voice modalities.

### Gaps

- **No first-party Meta / Llama engineering post on persona design** — all Llama-persona signal is either academic (Anthropic's Assistant-Axis paper analyzing Llama) or third-party.
- **No first-party DeepMind / Gemini character post.** DeepMind blogs focus on capability and modality (SIMA, Nano Banana, Gemini Robotics); Gemini's personality is documented only implicitly via product behavior and the name-origin post.
- **Replika has almost no engineering-blog output on persona design** — the corpus is community guides and help-center docs; there is no equivalent of Anthropic's character post from Replika despite its scale.
- **No public discussion of how base-model pretraining data shapes default persona.** *Partially addressed:* Anthropic's Persona Selection Model (Feb 2026) provides a theoretical account of how pretraining shapes the character distribution, and recommends introducing positive AI archetypes into training data. A data-level empirical analysis of *which* pretraining corpora produce which persona tendencies remains unpublished.
- **Limited first-party writing on persona evaluation.** OpenAI mentions "Model Spec Evals"; Anthropic mentions monitoring trait installation; but there is no industry post comparable to, say, capability benchmarks for persona.
- **Cross-cultural persona is under-discussed.** Every persona post is Anglophone, US-centric, and written in a voice that assumes a Western professional user. Humanization across languages and cultures is a near-complete blank in the industry-blog corpus.
- **Companion-vs-assistant boundary is undertheorized.** Pi, Replika, Kindroid, Character.AI all ship companion personas; Claude and ChatGPT ship assistant personas; few posts explicitly argue when a humanized assistant has crossed into companion territory and what obligations that creates. Anthropic's "I want to have a warm relationship… but I can't develop deep or lasting feelings" trait is the closest explicit statement.

---

## Source Summary

| # | Source | Org | Year |
|---|---|---|---|
| 1 | Claude's Character | Anthropic | 2024 |
| 2 | The Assistant Axis | Anthropic | 2026 |
| 3 | Claude's Constitution | Anthropic | 2023–24 |
| 4 | Prompt Design at Character.AI | Character.AI | 2024 |
| 5 | Introducing Prompt Poet | Character.AI | 2024 |
| 6 | Introducing the Model Spec | OpenAI | 2024 |
| 7 | Sycophancy in GPT-4o | OpenAI | 2025 |
| 8 | Expanding on Sycophancy | OpenAI | 2025 |
| 9 | Prompt Personalities (Cookbook) | OpenAI | 2025 |
| 10 | Introducing Pi | Inflection AI | 2023 |
| 11 | The Future of Pi | Inflection AI | 2023–24 |
| 12 | Claude's Character (commentary) | Simon Willison | 2024 |
| 13 | Prompt injection vs jailbreaking | Simon Willison | 2024 |
| 14 | What Makes an AI Companion Feel Like Your Person? | Kindroid | 2024 |
| 15 | Prompt Engineering for EVI + Voice Design | Hume AI | 2024–25 |
| 16 | Designing AI Agent Personas | Mindra | 2025 |
| 17 | Give Your AI Agent a Personality | AgentCraft | 2025 |
| 18 | Q&A with Amanda Askell | Yahoo/TechCrunch interview | 2024 |
| 19 | Grok system prompts / updates | xAI + The Decoder | 2024–25 |
| 20 | The Persona Selection Model | Anthropic | 2026 |

Total: **20 primary sources** across 9 organizations plus 3 independent practitioner voices. All sources were directly consulted for the synthesis above.
