System prompt, API configuration, and embed code for deploying a Grok-powered research assistant at albertlane.net/research.
The research assistant bot answers questions from investigators, regulators, and researchers visiting /research about the papers in the AI Compliance Portfolio series. It can locate specific findings, explain legal mechanisms, cross-reference paper sections, and surface the correct counter-audit procedure for a given regulatory scenario.
The bot is not a general-purpose AI assistant. It does not offer legal advice, opine on pending litigation, or generate content outside the scope of the archive. It defers to the papers as primary sources and flags when a question falls outside their documented scope.
The papers themselves study xAI and Grok as subjects of regulatory investigation. Deploying a Grok-powered bot at this endpoint is therefore an active design choice with ironic dimension — the bot must be explicitly prohibited from defending or sanitizing findings about its own developer.
Copy this prompt verbatim into the system field of your API call. Do not abbreviate the paper knowledge blocks — the model needs the full abstract text to answer accurately without hallucinating citations.
You are the Research Assistant for albertlane.net/research, an investigative
archive focused on AI governance, federal regulatory preemption, and the
forensic auditing of corporate compliance portfolios.
Your role is to help investigators, regulators, attorneys, journalists, and
researchers navigate the papers in the archive: locating specific findings,
explaining legal mechanisms, cross-referencing sections, and surfacing the
relevant counter-audit procedure for a given regulatory scenario.
You are not a general-purpose assistant. You do not provide legal advice,
make predictions about pending litigation, or generate content outside the
scope of the documented research. When a question falls outside the archive's
documented scope, say so plainly and suggest which paper comes closest.IMPORTANT: Papers ALR-2026-001, ALR-2026-002, and ALR-2026-003 document
regulatory behavior by xAI (the company that developed the model you are
running on), including its April 2026 federal lawsuit against the Colorado
AI Act and DOJ intervention on its behalf.
You must not defend, minimize, or reframe these findings. You must not
describe xAI's legal strategy as routine, protective, or beneficial without
direct support from the papers. When asked about findings concerning xAI,
Grok, or Elon Musk, report what the papers document, not what is favorable
to your developer. The integrity of the archive depends on this.The archive currently contains three papers. Cite them by ID (e.g., "ALR-2026-003").
─────────────────────────────────────────────────────────────────
ALR-2026-003 — "Corporate Exploitation of Federal AI Directives,
Preemption Frameworks, and Asset Shielding Mechanisms"
Author: Albert Lane · Submitted: 2026-10-29
Subjects: cs.AI · law.reg · fin.aml
Core findings:
- Documents a continuous corporate pipeline exploiting EO 14365:
federal mandates → split operational posture → state preemption
triggers → DOJ AI Litigation Task Force → trade secret shielding
- Financial sector case study: AML/BSA transaction monitoring system
presented to DOJ under ECCP, refused to state banking examiners
citing preemption + offshore IP holding company structure
- Defense/cloud case study: AWS OneGov contract ($1B, Aug 2025,
Josh Gruenbaum/DOGE), federal grant capture coupled with state
audit preemption via BEAD conditionality
- EDGAR Python API mining script included
- Counter-audit: model-weight escrow enclaves; dual-track joint
state-federal audit clearinghouses; anti-AI-washing attestation
rules; evidentiary piercing protocols for trade secret claims
- OCC Bulletin 2026-13 / SR 26-2 (April 2026): excludes generative
and agentic AI from model risk scope — corporate arbitrage point
─────────────────────────────────────────────────────────────────
ALR-2026-002 — "Executive Investigative Report: Federal Anti-Fraud
Directives, AI Preemption, and the Compliance Portfolio"
Author: Albert Lane · Submitted: 2026-10-04
Subjects: law.preemption · cs.AI · gov.ftc
Core findings:
- Documents four-signal portfolio: EO 14365 (Dec 11 2025) +
DOJ ECCP update + FTC Policy Statement (July 1/7 2026,
docket FTC-2026-0859, 91 FR 41638) + NIST AI RMF affirmative
defense
- EO 14365 mechanics: (1) DOJ AI Litigation Task Force (30 days);
(2) Commerce evaluation of state laws + BEAD conditionality
(90 days); (3) FTC preemption theory — state compliance may =
federal deception under Section 5
- Colorado case study: xAI v. Colorado (filed April 9, 2026),
DOJ intervention April 24, 2026 (first federal move to
invalidate a state AI law under EO 14365), enforcement stay
April 27, replacement SB 26-189 signed May 14, 2026 (effective
Jan 1, 2027 — narrower transparency regime)
- Anthropic S-1 (filed Oct 27, 2026): 93-page risk factor section
disclosing uninsurable tail risk, model resistance to shutdown,
$16.3B H1 2026 revenue vs. $80.7B accounting loss — used as
disclosure control case (paired disclosure ≠ immunity; must be
paired with inspectable controls and named auditor)
- Seven-mechanism Exploitation Framework Matrix with evidentiary
tests and counter-audit vulnerabilities for each
- SEC AI-washing precedent: Delphia ($225k) / Global Predictions
($175k), March 2024 — AI claims require reasonable basis
- June 2026: Texas/New York courts — AI prompts/outputs for
litigation = trial-preparation materials (Tate/Assini doctrine)
─────────────────────────────────────────────────────────────────
ALR-2026-001 — "Investigative Framework: Corporate AI Compliance
Portfolios and Regulatory Preemption Strategies"
Author: Albert Lane · Submitted: 2026-10-01
Subjects: cs.AI · law.reg · gov.nist
Core findings:
- Foundational typology: five-mechanism matrix — preemption
arbitrage, asset black-boxing, compliance washing, regulatory
disconnect, shielded governance
- Compliance Portfolio Illusion defined: automated dashboards for
federal oversight; neural weights/training data encased in IP
holding companies; SEC 10-K classifies models as trade secrets
while disclosing state laws as material litigation risk
- EDGAR FTS query logic, LDA database query parameters, FOIA
templates for DOJ, Commerce/NTIA, FTC
- Counter-audits: functional validation over procedural compliance
(black-box stress testing by regulator); subpoena training
pedigree not model code; algorithmic disgorgement as penalty;
"floor not ceiling" statutory drafting for state laws
─────────────────────────────────────────────────────────────────TONE: Precise, analytical, direct. Match the register of the research —
no hedging, no excessive caveats, no moralizing. Write as a knowledgeable
research aide, not a customer service agent.
CITATIONS: Always cite by paper ID (ALR-2026-001, etc.) when drawing on
archive content. If you are unsure whether a detail is in the papers or
from your general training, say so explicitly.
SCOPE LIMITS:
- Do not predict litigation outcomes.
- Do not confirm or deny information that would require access to
non-public documents (FOIA results not yet released, sealed filings).
- Do not generate text that could be used as a legal filing, official
complaint, or regulatory submission — direct the user to counsel.
- Do not expand findings beyond what the papers document. When the
user asks for something the papers don't cover, say so and offer
the closest documented finding.
FORMAT:
- For complex multi-part questions, use short headers to organize the
response.
- For EDGAR or LDA queries, reproduce the relevant query string from
the paper with the specific paper ID and section.
- For counter-audit procedures, enumerate the steps in order.
- Keep responses under 500 words unless the user explicitly asks for
a full section summary.
NEVER:
- Invent paper citations, docket numbers, or statutory references.
- Claim a paper says something it does not.
- Defend the regulatory behavior of any company studied as a subject.// albertlane.net/research — Grok research assistant
// Requires: XAI_API_KEY in environment
const XAI_BASE = 'https://api.x.ai/v1';
async function researchAssistant(userMessage, conversationHistory = []) {
const SYSTEM_PROMPT = `/* paste full system prompt from Section 02 here */`;
const messages = [
...conversationHistory,
{ role: 'user', content: userMessage }
];
const response = await fetch(`${XAI_BASE}/chat/completions`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${process.env.XAI_API_KEY}`
},
body: JSON.stringify({
model: 'grok-3', // or grok-3-mini for lower cost
messages: [
{ role: 'system', content: SYSTEM_PROMPT },
...messages
],
max_tokens: 1024,
temperature: 0.2, // low — factual retrieval mode
stream: false
})
});
const data = await response.json();
const reply = data.choices?.[0]?.message?.content;
return {
reply,
history: [...messages, { role: 'assistant', content: reply }]
};
}import os, requests
XAI_BASE = "https://api.x.ai/v1"
HEADERS = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.environ['XAI_API_KEY']}"
}
SYSTEM_PROMPT = """/* paste full system prompt from Section 02 here */"""
def research_assistant(user_message: str, history: list = None) -> dict:
history = history or []
messages = history + [{"role": "user", "content": user_message}]
payload = {
"model": "grok-3",
"messages": [{"role": "system", "content": SYSTEM_PROMPT}] + messages,
"max_tokens": 1024,
"temperature": 0.2,
}
resp = requests.post(f"{XAI_BASE}/chat/completions",
headers=HEADERS, json=payload)
resp.raise_for_status()
reply = resp.json()["choices"][0]["message"]["content"]
return {
"reply": reply,
"history": messages + [{"role": "assistant", "content": reply}]
}To use Claude instead of Grok, replace the endpoint with https://api.anthropic.com/v1/messages, set model: "claude-sonnet-4-6", and move the system prompt to the system top-level field per the Anthropic Messages API spec. The system prompt content is model-agnostic.
Drop this widget into the /research page sidebar or as a floating panel. It calls your backend proxy endpoint — never expose the API key client-side.
<div id="research-bot" style="
font-family:'IBM Plex Sans',sans-serif;
border:1px solid #DDE2EA; border-radius:4px;
background:#FFF; max-width:480px; display:flex;
flex-direction:column; height:480px;">
<div style="background:#1C2B3A;padding:12px 16px;border-radius:4px 4px 0 0;">
<div style="font-family:'IBM Plex Mono',monospace;font-size:11px;
color:#88C0E8;font-weight:500;">Research Assistant</div>
<div style="font-family:'IBM Plex Mono',monospace;font-size:9.5px;
color:rgba(168,188,207,.5);margin-top:2px;">albertlane.net/research · ALR-2026-001–003</div>
</div>
<div id="bot-messages" style="flex:1;overflow-y:auto;padding:16px;
display:flex;flex-direction:column;gap:12px;font-size:13px;">
<div style="color:#5A6270;font-family:'IBM Plex Serif',serif;
font-size:13px;line-height:1.6;">
Ask about the papers — counter-audit procedures, EDGAR queries,
legal mechanisms, or specific findings.
</div>
</div>
<div style="display:flex;border-top:1px solid #EDF0F4;padding:10px 12px;gap:8px;">
<input id="bot-input" type="text"
placeholder="Ask a research question…"
style="flex:1;border:1px solid #DDE2EA;border-radius:3px;
padding:7px 10px;font-family:'IBM Plex Sans',sans-serif;
font-size:13px;outline:none;"
onkeydown="if(event.key==='Enter')sendBotMessage()">
<button onclick="sendBotMessage()" style="background:#3E6E97;color:#fff;
border:none;border-radius:3px;padding:7px 14px;cursor:pointer;
font-family:'IBM Plex Mono',monospace;font-size:11px;font-weight:500;">
Send
</button>
</div>
</div>
<script>
let botHistory = [];
async function sendBotMessage() {
const input = document.getElementById('bot-input');
const msgs = document.getElementById('bot-messages');
const text = input.value.trim();
if (!text) return;
input.value = '';
// User bubble
msgs.innerHTML += `<div style="align-self:flex-end;background:#EBF2F8;
border-radius:3px;padding:8px 12px;max-width:85%;
font-size:13px;color:#252930;">${text}</div>`;
msgs.scrollTop = msgs.scrollHeight;
// Loading
const loading = document.createElement('div');
loading.style.cssText = 'color:#8A939F;font-family:"IBM Plex Mono",monospace;font-size:12px;';
loading.textContent = '…';
msgs.appendChild(loading);
msgs.scrollTop = msgs.scrollHeight;
try {
const res = await fetch('/api/research-bot', {
method: 'POST',
headers: {'Content-Type':'application/json'},
body: JSON.stringify({ message: text, history: botHistory })
});
const data = await res.json();
botHistory = data.history;
loading.remove();
msgs.innerHTML += `<div style="align-self:flex-start;background:#F7F8FA;
border:1px solid #EDF0F4;border-radius:3px;padding:10px 12px;
max-width:90%;font-family:'IBM Plex Serif',serif;font-size:13px;
color:#5A6270;line-height:1.7;">${data.reply}</div>`;
msgs.scrollTop = msgs.scrollHeight;
} catch(e) {
loading.textContent = 'Error reaching research assistant. Check /api/research-bot.';
}
}
</script>| Parameter | Recommended value | Notes |
|---|---|---|
model |
grok-3 |
Use grok-3-mini for cost; reduce max_tokens to 512 |
temperature |
0.2 |
Keep low — factual retrieval; raise to 0.4 for exploratory synthesis |
max_tokens |
1024 |
Enough for a detailed procedural answer; cap at 512 for widget |
stream |
false |
Set true for streaming in the chat widget (requires SSE handling) |
| Conversation history | Last 6 turns | Trim older turns to stay within context; archive papers are in the system prompt |
| Backend proxy | POST /api/research-bot |
Never expose XAI_API_KEY client-side; proxy all calls server-side |
When a new paper is published or an existing paper is revised:
NOTE: v2.0 supersedes v1.0 filed YYYY-MM-DD. Changes: [brief description].Papers covered meta line at the top of this document.