> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mavera.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Product Requirements → Focus Group
### Scenario
Product managers write PRDs in Notion — feature descriptions, user stories, success metrics, and launch plans. Before committing engineering resources, you want synthetic validation: How would different user personas rate their interest? What objections would they raise? This job pulls PRD pages, creates user personas, and runs a Focus Group where each persona evaluates the proposed feature.
**Flow:** Notion `GET /blocks/{page_id}/children` (PRD pages) → Extract requirements → Mavera `POST /api/v1/personas` → `POST /api/v1/focus-groups` → Interest ratings per persona
### Code
```python Python theme={"dark"}
import os, requests, time
NOTION = os.environ["NOTION_API_KEY"]
MV = os.environ["MAVERA_API_KEY"]
NB = "https://api.notion.com/v1"
MB = "https://app.mavera.io/api/v1"
NH = {
"Authorization": f"Bearer {NOTION}",
"Notion-Version": "2022-06-28",
"Content-Type": "application/json",
}
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}
PRD_DB_ID = "your-prd-database-id"
# 1. Query PRD pages in "Review" status
prds = requests.post(f"{NB}/databases/{PRD_DB_ID}/query", headers=NH, json={
"filter": {"property": "Status", "select": {"equals": "In Review"}},
"page_size": 5,
}).json().get("results", [])
print(f"Found {len(prds)} PRDs in review")
# 2. Extract PRD content
def get_page_text(page_id):
texts = []
cursor = None
while True:
params = {"page_size": 100}
if cursor:
params["start_cursor"] = cursor
r = requests.get(f"{NB}/blocks/{page_id}/children", headers=NH, params=params)
if r.status_code == 429:
time.sleep(1); continue
data = r.json()
for block in data.get("results", []):
btype = block.get("type", "")
rt = block.get(btype, {}).get("rich_text", [])
text = "".join(t.get("plain_text", "") for t in rt)
if text.strip():
texts.append(text)
cursor = data.get("next_cursor")
if not cursor:
break
time.sleep(0.4)
return "\n".join(texts)
# 3. Create user personas for validation
USER_SEGMENTS = [
{"name": "Enterprise IT Director", "desc": "Manages 50+ person tech team. Evaluates tools for security, scalability, and ROI. Risk-averse. Needs exec-level justification."},
{"name": "Startup Founder", "desc": "Wears many hats. Needs fast time-to-value. Price-sensitive but willing to pay for 10x improvements. Values simplicity."},
{"name": "Marketing Manager (Mid-Market)", "desc": "Runs campaigns for a 200-person company. Juggles 5+ tools. Wants consolidation and better reporting. Reports to VP Marketing."},
{"name": "Developer (IC)", "desc": "Individual contributor building integrations. Cares about API quality, documentation, and developer experience. Skeptical of 'AI magic' claims."},
{"name": "Product Analyst", "desc": "Data-driven decision maker. Wants quantitative insights, A/B testing support, and export capabilities. Lives in dashboards."},
]
persona_ids = []
for seg in USER_SEGMENTS:
p = requests.post(f"{MB}/personas", headers=MH, json={
"name": f"PRD Review: {seg['name']}",
"description": seg["desc"],
}).json()
persona_ids.append({"id": p["id"], "name": seg["name"]})
time.sleep(0.3)
print(f"Created {len(persona_ids)} personas")
# 4. Run focus group for each PRD
for prd in prds:
props = prd.get("properties", {})
title_parts = props.get("Name", props.get("Title", {})).get("title", [])
title = "".join(t.get("plain_text", "") for t in title_parts)
prd_text = get_page_text(prd["id"])
fg = requests.post(f"{MB}/focus-groups", headers=MH, json={
"name": f"PRD Review: {title}",
"persona_ids": [p["id"] for p in persona_ids],
"questions": [
f"Here is a product requirements document:\n\n{prd_text[:3000]}\n\nOn a scale of 1-10, how interested would you be in this feature? Explain your rating.",
"What is the single biggest concern or objection you have about this feature?",
"What would need to be true for you to adopt this on day one?",
"How would you describe this feature to a colleague in one sentence?",
"What existing alternative (if any) do you currently use to solve this problem?",
],
"responses_per_persona": 2,
}).json()
# 5. Poll for results
for _ in range(30):
time.sleep(5)
data = requests.get(f"{MB}/focus-groups/{fg['id']}", headers=MH).json()
if data.get("status") == "completed":
break
print(f"\n{'='*60}\nPRD: {title}\n{'='*60}")
for resp in data.get("responses", []):
persona_name = next((p["name"] for p in persona_ids if p["id"] == resp.get("persona_id")), "Unknown")
print(f"\n[{persona_name}] Q: {resp.get('question','')[:80]}...")
print(f" A: {resp.get('answer','')[:300]}")
```
```javascript JavaScript theme={"dark"}
const NOTION = process.env.NOTION_API_KEY;
const MV = process.env.MAVERA_API_KEY;
const NB = "https://api.notion.com/v1";
const MB = "https://app.mavera.io/api/v1";
const NH = {
Authorization: `Bearer ${NOTION}`,
"Notion-Version": "2022-06-28",
"Content-Type": "application/json",
};
const MH = { Authorization: `Bearer ${MV}`, "Content-Type": "application/json" };
const PRD_DB_ID = "your-prd-database-id";
// 1. Query PRDs
const prds = await fetch(`${NB}/databases/${PRD_DB_ID}/query`, {
method: "POST", headers: NH,
body: JSON.stringify({
filter: { property: "Status", select: { equals: "In Review" } },
page_size: 5,
}),
}).then(r => r.json()).then(d => d.results || []);
console.log(`Found ${prds.length} PRDs in review`);
// 2. Extract page text
async function getPageText(pageId) {
const texts = [];
let cursor = null;
do {
const params = new URLSearchParams({ page_size: "100" });
if (cursor) params.set("start_cursor", cursor);
const res = await fetch(`${NB}/blocks/${pageId}/children?${params}`, { headers: NH });
if (res.status === 429) { await new Promise(r => setTimeout(r, 1000)); continue; }
const data = await res.json();
for (const block of data.results || []) {
const rt = block[block.type]?.rich_text || [];
const text = rt.map(t => t.plain_text).join("");
if (text.trim()) texts.push(text);
}
cursor = data.next_cursor;
await new Promise(r => setTimeout(r, 400));
} while (cursor);
return texts.join("\n");
}
// 3. Create personas
const USER_SEGMENTS = [
{ name: "Enterprise IT Director", desc: "50+ person team. Security, scalability, ROI." },
{ name: "Startup Founder", desc: "Fast time-to-value. Price-sensitive. Simplicity." },
{ name: "Marketing Manager", desc: "Mid-market. Juggles 5+ tools. Wants consolidation." },
{ name: "Developer (IC)", desc: "API quality, docs, DX. Skeptical of AI hype." },
{ name: "Product Analyst", desc: "Data-driven. A/B testing. Dashboards." },
];
const personaIds = [];
for (const seg of USER_SEGMENTS) {
const p = await fetch(`${MB}/personas`, { method: "POST", headers: MH,
body: JSON.stringify({ name: `PRD Review: ${seg.name}`, description: seg.desc }),
}).then(r => r.json());
personaIds.push({ id: p.id, name: seg.name });
await new Promise(r => setTimeout(r, 300));
}
// 4. Focus group per PRD
for (const prd of prds) {
const props = prd.properties || {};
const titleProp = props.Name || props.Title || {};
const title = (titleProp.title || []).map(t => t.plain_text).join("") || "Untitled";
const prdText = await getPageText(prd.id);
const fg = await fetch(`${MB}/focus-groups`, { method: "POST", headers: MH,
body: JSON.stringify({
name: `PRD Review: ${title}`,
persona_ids: personaIds.map(p => p.id),
questions: [
`PRD:\n\n${prdText.slice(0, 3000)}\n\n1-10 interest rating with explanation.`,
"Biggest concern or objection?",
"What must be true for day-one adoption?",
"Describe this feature in one sentence to a colleague.",
"Current alternative for this problem?",
],
responses_per_persona: 2,
}),
}).then(r => r.json());
// 5. Poll
let data;
for (let i = 0; i < 30; i++) {
await new Promise(r => setTimeout(r, 5000));
data = await fetch(`${MB}/focus-groups/${fg.id}`, { headers: MH }).then(r => r.json());
if (data.status === "completed") break;
}
console.log(`\n${"=".repeat(60)}\nPRD: ${title}`);
for (const resp of data.responses || []) {
const name = personaIds.find(p => p.id === resp.persona_id)?.name || "Unknown";
console.log(`\n[${name}] Q: ${(resp.question || "").slice(0, 80)}...`);
console.log(` A: ${(resp.answer || "").slice(0, 300)}`);
}
}
```
### Example Output
```text theme={"dark"}
Found 2 PRDs in review
Created 5 personas
============================================================
PRD: Real-Time Collaboration Dashboard
[Enterprise IT Director] Q: On a scale of 1-10, how interested would you be...
A: 7/10. Real-time collaboration is valuable but I need to understand the
security model first. Does this support SSO? Can I restrict which dashboards
are shared externally? Without granular permissions, this is a non-starter
for regulated industries.
[Startup Founder] Q: On a scale of 1-10, how interested would you be...
A: 9/10. This is exactly what we need. We're currently screenshotting
dashboards and pasting them into Slack. Real-time would save our team
2-3 hours per week in alignment meetings alone.
[Developer (IC)] Q: Biggest concern or objection?
A: WebSocket-based real-time at scale is hard. What's the latency
guarantee? What happens when connections drop? I'd want to see
the API spec before committing any integration work.
[Product Analyst] Q: Current alternative?
A: We export CSVs to Google Sheets and share those. It's clunky but
everyone knows Sheets. You'd need to be significantly better than
"good enough" to justify a switch.
```
### Error Handling
Long PRDs may exceed the Focus Group context limit. The code truncates to 3000 chars. For detailed PRDs, summarize with Mave first, then pass the summary as context.
5 personas × 5 questions × 2 responses = 50 responses. This may take 2+ minutes. The polling loop allows up to 150 seconds. Increase for larger configurations.
PRD databases vary widely in schema. The code assumes a `Status` select and `Name`/`Title` title property. Inspect with `GET /databases/{id}` to verify your schema.