Documentation Index
Fetch the complete documentation index at: https://docs.buildpersona.ai/llms.txt
Use this file to discover all available pages before exploring further.
Let’s build something real: an AI coach that actually knows its users.
The Problem
Most AI coaching apps have the memory of a goldfish. Every session starts fresh:
User: "I'm stressed about the project deadline"
Coach: "What deadline are you referring to?"
User: "...the one I've told you about for the past 3 weeks"
- What actually works for them (psyche)
- What they’re actively working on (goals)
- The full arc of their journey (episodes)
The Persona Approach
With Persona, the coach has real context:
# User opens the app
context = await persona.get_context(user_id, "coaching session")
# Coach knows immediately:
# - Active goal: "Ship auth feature by Friday"
# - Psyche: "Works best in morning, struggles after lunch"
# - Recent episode: "Felt overwhelmed yesterday, couldn't focus"
response = coach_llm(
user_message,
system=f"You're a productivity coach. Context: {context}"
)
“I know you’ve got that auth feature deadline coming up Friday.
Last time you felt this overwhelmed, breaking it into morning
blocks really helped. Want to try that approach again?”
Real personalization, not generic advice.
Implementation
1. Ingest Coaching Sessions
After each conversation:
async def log_session(user_id: str, messages: list):
transcript = "\n".join([
f"{m['role']}: {m['content']}"
for m in messages
])
await persona.ingest(
user_id=user_id,
content=transcript,
source_type="coaching_session"
)
- What they talked about (episode)
- Any revealed preferences or struggles (psyche)
- Any commitments or action items (goals)
2. Build Context-Aware Responses
On each session:
async def get_coach_response(user_id: str, user_message: str):
# Get full context
context = await persona.rag_query(
user_id=user_id,
query=f"relevant context for: {user_message}"
)
# Generate personalized response
response = await llm.chat([
{"role": "system", "content": f"""
You're a personalized productivity coach.
{context}
Use this context naturally. Reference specific things
the user has mentioned before. Acknowledge their patterns.
Don't be creepy about it - weave it in naturally.
"""},
{"role": "user", "content": user_message}
])
return response
3. Track Progress Programmatically
Use Ask to extract structured progress:
async def get_weekly_summary(user_id: str):
return await persona.ask(
user_id=user_id,
query="Weekly progress summary",
output_schema={
"goals_progressed": ["goal description"],
"blockers_identified": ["blocker"],
"patterns_observed": ["pattern"],
"suggested_focus": "string"
}
)
What Makes This Different
Without Persona:
User: "I can't focus today"
Coach: "Here are 5 tips for improving focus:
1. Take breaks...
2. Minimize distractions..."
(Generic advice anyone could give)
User: "I can't focus today"
Coach: "Is it the afternoon slump again? I've noticed you
mentioned this a few times — you seem to consistently
lose steam after lunch.
Last week you tried doing creative work in the morning
and admin tasks after lunch. That seemed to work well
for the presentation you finished. Worth trying that
pattern again?"
(Specific, evidence-based, personalized)
Results
With Persona, your coach can:
| Capability | How |
|---|
| Remember history | Episodes chain sessions together |
| Know preferences | Psyche accumulates patterns |
| Track goals | Goals persist and update |
| Measure progress | Ask API extracts structured insights |
| Personalize deeply | Context-aware responses every time |
Try It
Start with the quickstart, then:
- Create a test user
- Ingest a few coaching session transcripts
- Query and see the context that comes back
- Build your first personalized response
The difference is immediate.
