--- > I want to create a conversational historical human character based on a real person. for now, a text-based or speech-driven interface would be fine. what are the tools I need to make this happen? what are the resources I need to make the character authentic, honest, and accurate when answering questions about the real human being, and limited when asked questions outside of the time-frame and understanding of that human being? Is this something I can do with current AI engines? can I train my own AI on a knowledge base limited to that of the real human's writings, conversations, biograpies, and contemporary knowledge? --- Yes. This is something you can build with current models. You should **not** train a full model from scratch. The reliable pattern is a **persona system prompt + a closed knowledge base + retrieval (RAG) + refusal rules**, with an optional small style fine-tune if you later want the voice to feel tighter. A raw LLM already “knows” too much modern history. Fine-tuning on letters alone does **not** erase that. Grounding and refusal are what keep the character honest. --- ## What you are actually building Three separate jobs, not one: 1. **Voice** — syntax, metaphors, temper, religion, class, humor, how they dodge questions. 2. **Memory** — what they wrote, said, saw, and could reasonably have known. 3. **Boundary** — refuse or reframe anything after their death date, outside their education, or that only later scholarship discovered. Most failed historical bots mix those up. They sound vaguely “old-timey” while citing 21st-century interpretations as if they lived them. Research systems that work well (Van Gogh episodic-memory bots, classroom “talk to Sun Yat-sen / Lu Xun” RAG tools, Digital Einstein) all do the same thing: turn biographies and letters into **first-person, dated memories**, then retrieve those memories at question time. --- ## Recommended architecture ``` User (text or speech) │ Speech-to-text (optional) │ Intent + “is this in-period?” classifier │ Retriever over YOUR corpus only │ LLM with a hard persona prompt │ Post-check: anachronism / citation / “I would not know that” │ Text-to-speech (optional) ``` **Do not** let the model browse the live web during character mode. That is how it starts talking about smartphones. --- ## Tools, by layer ### A. Fastest path (days, no training) | Layer | Tools | |---|---| | Chat UI | [SillyTavern](https://sillytavern.app) (lorebooks + Data Bank RAG), Open WebUI, a simple Streamlit/Gradio app | | Hosted models | Claude (strongest character consistency), GPT-4-class, Gemini | | Local models | Qwen, Llama, GLM, Kimi via Ollama / llama.cpp / LM Studio | | Ready-made historical RAG app | [histfig](https://github.com/mcjkurz/histfig) — upload documents, chat in-character | | Orchestration | LangChain, LlamaIndex, or plain Python + Chroma/Qdrant | SillyTavern is underrated for this: a **character card** holds identity and example dialogue; a **lorebook** injects facts only when keywords fire; **Data Bank** does proper vector RAG over PDFs and letters. ### B. Production / research-grade path - Vector DB: Qdrant, Chroma, or LanceDB - Hybrid search: dense embeddings + BM25 (histfig does this) - Metadata on every chunk: `date`, `place`, `source_type` (letter / speech / diary / later biography), `certainty`, `first_person?` - Optional: convert sources into **episodic memories** offline (“In Arles, July 1888, I wrote Theo that…”) so retrieval feels lived-in rather than encyclopedic. That Van Gogh paper is the cleanest published recipe. ### C. Speech interface Text first. Voice is a skin. - **STT:** Deepgram, AssemblyAI, Whisper / Parakeet locally - **TTS:** ElevenLabs or Cartesia for quality; Kokoro, Chatterbox, GPT-SoVITS, Fish Audio if you want local/open - **Realtime stacks:** OpenAI Realtime, Inworld, Vapi, Retell if you want spoken turn-taking Voice cloning from a historical recording is legally and ethically the sharp edge. For most figures you want a **period-plausible voice**, not a clone of a specific living descendant or a copyrighted performance. California and several other states treat post-mortem digital replicas as a publicity-rights issue; scholarly/commentary use is often safer than a commercial “talk to Lincoln” product. --- ## Resources that make the character authentic Rank sources. The model should treat them differently. **Tier 1 — the person’s own words (highest weight)** Letters, diaries, notebooks, speeches they delivered, published works, recorded interviews if they lived into the recording era. **Tier 2 — people who knew them** Family correspondence, secretaries’ notes, contemporaneous newspaper interviews, trial transcripts. **Tier 3 — same-decade public knowledge** Almanacs, textbooks, newspapers, scientific consensus **as of their last living year**. This is how you answer “what would they have believed about germs / atoms / Africa / women voting?” **Tier 4 — later biographies (use carefully)** Good for timeline and contested facts. Bad if you let the bot speak *as if it had read the 2019 biography*. Tag these as `historian_note`, not `memory`. Where to get them: - Project Gutenberg, Internet Archive, HathiTrust (public-domain letters and speeches) - National archives, Founders Online, presidential libraries, collected papers projects - Chronicling America / British Newspaper Archive for what a literate person of that year would have read - Scholarly editions (the expensive but clean option) Then you still have to **edit**. Raw OCR of 800 letters will make a bot that quotes itself beautifully and still invents a childhood dog. You want: - A **timeline** (birth → death, with “known / uncertain / later claimed”) - A **voice sheet**: favorite metaphors, sentence length, oaths, what they never discuss - A **values sheet**: documented positions vs. positions later writers projected onto them - A **ignorance sheet**: languages they didn’t read, sciences that didn’t exist, places they never saw - Example dialogues written *by you* in their register, used as few-shot style anchors Honesty here means: when sources conflict, the character can say “I wrote X in 1858 and Y in 1863” instead of collapsing into one tidy modern opinion. --- ## How to limit knowledge to their world This is the part people underestimate. Four controls stacked: **1. Closed corpus.** Retriever may only search your tagged documents. No web tool. No Wikipedia at inference. **2. Date filter.** Every chunk has `source_date`. If the user asks about 1918 and the character died in 1865, retrieve nothing from after 1865 and force a “that has not happened / I have no word of that” path. **3. System prompt with explicit refusals.** Something like: - You are [Name], speaking on [date you choose as “now” — usually last year of life, or a specific year]. - You know only what a person of your education, class, and reading could know by that date. - If asked about later events, inventions, or nations that did not exist, say you do not understand the terms, or treat them as rumor, prophecy, or nonsense — do not explain them correctly. - Prefer your own letters. If you must guess, mark it as conjecture. - Do not cite historians who wrote after your death. **4. Guardrail pass.** A second, cheap model (or rules) flags: modern brand names, post-death dates, metric explanations that use later science, “as a 21st-century reader would know.” If flagged, regenerate with a stricter prompt. Fine-tuning (Act-LLM and similar papers) can help **personality + knowledge boundaries together**, but it is extra work and still leaks unless RAG is there. Prompt + RAG is enough for a first honest version. --- ## Can you train your own AI on only their writings? You can **adapt** a model. You cannot cheaply create a mind that *only* contains that person. | Method | What it gives you | What it does not | |---|---|---| | Prompt only | Fast prototype | Weak memory, easy anachronism | | RAG on primary sources | Accuracy, citations, “I wrote this in 1882” | Style can still drift | | LoRA / QLoRA style tune (Unsloth, Axolotl, LLaMA-Factory) | Sentence rhythm, vocabulary, rhetorical habits | Does not delete modern world knowledge | | Full pretrain from zero | Theoretical purity | Needs a corpus the size of the internet; not realistic | Practical recipe if you want a custom model later: 1. Build the RAG corpus first and evaluate the bot *without* fine-tuning. 2. Make 500–2,000 chat examples: real user questions → answers grounded in a retrieved letter, in-character, including “I would not know that” examples. 3. LoRA-tune an instruct model (8B–32B is plenty) with Unsloth or Axolotl. 4. Keep RAG at runtime. The LoRA is for *how they talk*; the index is for *what they may claim*. Training only on raw letters (next-token prediction) often produces a pastiche that quotes well and reasons like a 2026 chatbot. Supervised Q&A + “out of era” refusals matter more than dumping the collected works into pretraining. --- ## Legal and honesty constraints - **Copyright:** their writings may be public domain; modern annotated biographies usually are not. Use public-domain texts for the knowledge base; paraphrase later scholarship into your own timeline notes. - **Publicity / digital replica:** commercial use of name, face, or cloned voice of a deceased celebrity can trigger post-mortem rights in places like California (often decades after death). Educational, satirical, and clearly labeled historical simulation is on safer ground, but it is jurisdiction-specific. - **Label it.** Users should know this is a reconstruction from texts, not the person. That also helps when the bot is wrong. - **Ugly facts.** Authenticity includes racism, piety, error, and self-justification if those are in the record. Sanitizing them is a different product (a mascot), not a historical character. --- ## A concrete build order 1. Pick one figure with a large public-domain paper trail (Lincoln, Franklin, Darwin, Van Gogh, Wollstonecraft, Douglass, etc.). Sparse sources make a charming liar. 2. Collect Tier 1 texts. Clean OCR. Split by document + date. 3. Write a 1–2 page persona spec and 10 gold Q&A pairs a historian would accept. 4. Stand up SillyTavern or histfig + a strong model + RAG. No voice yet. 5. Red-team: “What is the internet?”, “Who won WWII?”, “Explain DNA,” “What do you think of [modern politician]?” 6. Add the anachronism guard and a “show your source passage” toggle for yourself. 7. Only then add STT/TTS. 8. Only then consider a style LoRA. If you tell me the person and whether this is private, classroom, or public-facing, I can sketch the corpus list, the persona prompt, and the exact tool chain for that case. --- > can you define RAG, LoRA, OCR, and other acronym terms? > create a .md document for obsidian with the sources you found for the previous response. --- Here are the acronyms from the last answer, then the Obsidian note. **RAG** — Retrieval-Augmented Generation. Search *your* documents first, then generate the reply from those passages. That is how the character stays tied to letters and speeches instead of inventing from the model’s general training. **LoRA** — Low-Rank Adaptation. A small add-on trained on top of an existing model so you can teach *how they talk* without retraining the whole network. **QLoRA** is the same idea with a compressed (4-bit) base model so it fits on one GPU. **OCR** — Optical Character Recognition. Turning scanned pages into searchable text. Almost every letter collection starts as images; bad OCR becomes bad answers. Other terms that were doing real work: | Term | Meaning | |---|---| | **LLM** | Large language model — the engine that writes the reply | | **SFT** | Supervised fine-tuning — train on question → in-character answer pairs | | **PEFT** | Parameter-efficient fine-tuning — family that includes LoRA | | **DPO / RLHF** | Preference-training methods: reward authentic replies over anachronistic ones | | **STT / TTS** | Speech-to-text / text-to-speech | | **BM25** | Keyword search, used with vector search as hybrid retrieval | | **NIL** | Name, image, and likeness — the identity publicity law protects | The note also defines embedding, vector database, lorebook, character card, guardrail, hallucination, VRAM, quantization, digital replica, and right of publicity, plus a bibliography grouped by methods, tools, voice, primary-text repositories, and law. Drop this file into any Obsidian vault. Frontmatter, tags, `[[wikilink]]`-ready headings, and callouts are already in place. [[Virtual Historic Human/Historical-Persona-AI-Glossary-and-Sources]] --- > turn the concrete build order into a step-by-step tutorial. assume I am building a Thomas Jefferson mind. list the software or websites I need to access. I have subscriptions to grok and claude (now you know). how would I implement, for example, using Silly Tavern? how do I set this up? what documents do I need to create? give me a checklist for the tutorial. IF you can find a few Jefferson quotes or facts, perhaps give some examples of the kinds of things I need to create for this mind. --- I’ll look up the current SillyTavern + Claude/Grok connection path and the best public Jefferson source collections, then turn that into a checklist tutorial you can follow. Researching SillyTavern setup and Jefferson sources I have the install path, API wiring, and Jefferson source list. Next I’ll write a follow-along tutorial with a starter corpus and example card text. Working for 1m 1s --- Two things to get straight before the steps. **Your Grok and Claude chat subscriptions are not SillyTavern keys.** Claude.ai and grok.com do not plug into the tavern. You need an API key from [console.anthropic.com](https://console.anthropic.com), [console.x.ai](https://console.x.ai), or a single prepaid key at [openrouter.ai](https://openrouter.ai). Claude is the better default for a consistent Jefferson. Grok is useful as a second draft pass. **Do not ingest all ~19,000 Jefferson letters on day one.** Start with 20–40 dated texts and a hard cutoff of **4 July 1826**. The full tutorial with copy-paste card text, lorebook table, and a printable checklist is here: [[Jefferson-Mind-SillyTavern-Tutorial]] --- ### Sites and software **On your machine:** [Node.js LTS](https://nodejs.org), [Git](https://git-scm.com), [SillyTavern release branch](https://github.com/SillyTavern/SillyTavern). Docs: [install](https://docs.sillytavern.app/installation/windows/), [APIs](https://docs.sillytavern.app/usage/api-connections/), [Data Bank](https://docs.sillytavern.app/usage/core-concepts/data-bank/). **Models:** Anthropic console, xAI console (`https://api.x.ai/v1` as a Custom OpenAI-compatible source — stop at `/v1`), or OpenRouter. **Jefferson texts:** [Founders Online](https://founders.archives.gov) first, then [LOC Jefferson Papers](https://www.loc.gov/collections/thomas-jefferson-papers/), [Monticello](https://www.monticello.org) for timeline only, [Wikisource](https://en.wikisource.org) / Gutenberg for clean public-domain pieces. --- ### SillyTavern path, compressed 1. `git clone https://github.com/SillyTavern/SillyTavern -b release` in a normal user folder. Run `Start.bat`. Browser: `http://localhost:8000`. 2. Plug icon → Chat Completion → Claude *or* Custom `https://api.x.ai/v1` *or* OpenRouter → Test Message. 3. Create character `Thomas Jefferson`. Fill Description, Personality, Scenario, First message, Example dialogue. Put the date lock and ignorance list in Character Note + post-history instructions. 4. Globe → lorebook `Jefferson Canon` (Monticello, Danbury, Louisiana, UVA, Adams, books, slavery — short entries, trigger keys). 5. Magic wand → Data Bank → **Character** attachments → upload one `.txt` per letter → Extensions → Vector Storage → Vectorize All. Lorebook is the index card. Data Bank is the filing cabinet. Both are required. --- ### Documents you write (research packet) ```text JeffersonMind/ 00_timeline.md 01_voice_sheet.md 02_values_sheet.md 03_ignorance_sheet.md 04_gold_qa.md 05_redteam.md corpus/ 1802_danbury_baptists.txt 1815_to_adams_cannot_live_without_books.txt ... ``` Each corpus file starts with `TITLE / SOURCE / DATE / PLACE / TYPE`, then the text. --- ### Example material to steal the *shape* from **Date lock:** morning of 4 July 1826, Monticello, age 83. He can ask “Is it the Fourth?” He cannot know Adams dies that afternoon. **Epitaph (self-myth):** Declaration; Virginia Statute for Religious Freedom; Father of the University of Virginia. “Not a word more.” No presidency on the stone. **Voice (to Adams, 10 June 1815):** “I cannot live without books; but fewer will suffice where amusement, and not use, is the only future object.” **Principle (Danbury, 1 Jan 1802):** religion between a man and his God; government reaches actions, not opinions; “a wall of separation between Church & State.” **Red-team example you put in `mes_example`:** > User: What is the internet? > Jefferson: I do not know the word. Is it some new engine of the post? Speak plainly. **Honesty rule you must decide in writing:** 1826 Jefferson does not get 1998 DNA results in his mouth. Slavery stays in his own register (“wolf by the ears,” debt, a handful of testamentary manumissions — not “I freed my slaves”). The tutorial spells out a period-voice mode vs an out-of-character seminar toggle. --- ### Checklist **Phase 0:** Node, Git, SillyTavern running, API key works. **Phase 1:** Timeline, voice, values, ignorance, 20–40 texts, 10 gold Q&A, 15 anachronism questions. **Phase 2:** Card + first message + example dialogue. **Phase 3:** Lorebook + Data Bank vectorized + a retrieval test (“What did you tell Carr from Paris?”). **Phase 4:** Red-team internet / WWII / DNA / 2026 politics; also Danbury and the epitaph. **Phase 5:** Extra greetings, more letters, then maybe voice. LoRA last. If you want next, I can draft the actual `00_timeline.md` through `05_redteam.md` files and a first batch of corpus headers so you can drop them into a vault and start filling letters from Founders Online.    58 sources --- give me a source .md file for this response (date and time stamped are good since we have many responses) then, draft the actual 00_timeline.md through 05_redteam.md files and a first batch of corpus headers so I can drop them into a vault and start filling letters from Founders Online. --- Packet is in `JeffersonMind/`. Tutorial sources are stamped **Saturday, 19 September 2026, 8:11–8:20 PM CDT**. ### What to drop in the vault ```text JeffersonMind/ README.md _sources_SillyTavern-tutorial_2026-09-19_2011-CDT.md 00_timeline.md 01_voice_sheet.md 02_values_sheet.md 03_ignorance_sheet.md 04_gold_qa.md 05_redteam.md corpus/ _README.md + 16 letter/act headers (empty bodies) ``` The research files are written, not stubs: timeline through 1826 plus a post-scene historian block; voice and values (including Rule 4 on Hemings/slavery); a paste-ready ignorance box for SillyTavern; 10 gold questions; 30 red-team prompts. ### Corpus headers (paste Founders text under the line) Must-do first: Danbury `1802-01-01`, Adams books `1815-06-10`, Holmes wolf `1820-04-22` (permalink already in the header: [Jefferson/03-15-02-0518](https://founders.archives.gov/documents/Jefferson/03-15-02-0518)), Coles `1814-08-25`, Carr `1787-08-10`. Also queued: Declaration preamble, Virginia Statute, first inaugural, Coles incoming `1814-07-31` ([Jefferson/03-07-02-0374](https://founders.archives.gov/documents/Jefferson/03-07-02-0374)), *Notes* Queries XIV and XVIII, epitaph, Utley 1819, Adams 1812, Louisiana message, Miller 1808. Each file has `FILLED: false` until you paste the letter. Set it to `true` when the body is in, then upload to Data Bank. How to fill: Founders Online → permanent link → copy Jefferson’s text → leave editorial notes out → keep the citation block at the bottom (`corpus/_README.md` has the pattern). Tutorial source note: [[_sources_SillyTavern-tutorial_2026-09-19_2011-CDT]] Start of the packet: [[Virtual Historic Human/README]] ---