The Problem
On Monday you tested the 3 prompts in ChatGPT. Great! You saw how extraction → validation → personalization works. But here's the reality: you can't ask your HR team to copy-paste for every new hire. One coordinator spending 3 hours manually running prompts per hire? That's $90 per hire in labor costs. Multiply that across 50 hires per month and you're looking at $54,000/year just on onboarding admin. Plus the inconsistencies that lead to incomplete paperwork and frustrated new employees.
See It Work
Watch the 3 prompts chain together automatically. This is what you'll build.
Watch It Work
See the AI automation in action
The Code
Three levels: start simple, add reliability, then scale to production. Pick where you are.
When to Level Up
Simple API Calls
- Direct OpenAI/Claude API calls
- Sequential processing (extract → validate → personalize)
- Manual HRIS entry
- Email-based communication
- Spreadsheet tracking
Add Integrations
- HRIS API integration (BambooHR, Workday)
- Slack/Teams bot for welcome messages
- Error handling and retries
- Logging for audit trail
- Basic analytics dashboard
Framework Orchestration
- Multi-step workflow with branching logic
- Parallel processing where possible
- Survey automation (day 7, 30, 90)
- Sentiment analysis on responses
- Manager notifications and escalations
- Advanced analytics and reporting
Multi-Agent System
- Dedicated agents: extraction, validation, personalization, compliance
- Load balancing across multiple LLM providers
- Real-time monitoring and alerting
- A/B testing of prompts and workflows
- Predictive analytics (churn risk, engagement scores)
- Custom integrations with any HRIS/ATS
HR-Specific Gotchas
Real challenges you'll face in production. Here's how to handle them.
PII Handling & Data Privacy
Redact PII before sending to LLM. Use regex or AWS Comprehend for detection. Store sensitive data separately in encrypted database.
# Redact PII before LLM processing
import re
from typing import Dict
def redact_pii(text: str) -> Dict[str, str]:
"""Redact SSN, email, phone before sending to LLM"""
redacted = text
Multi-Language Support
Use language detection + translation APIs. Or use multilingual models like Claude 3.5 Sonnet which natively supports 100+ languages.
# Multi-language onboarding
from langdetect import detect
from deep_translator import GoogleTranslator
def process_multilingual(hire_text: str) -> Dict:
"""Detect language and process accordingly"""
detected_lang = detect(hire_text)
Handling Incomplete Data
Build a progressive data collection workflow. Start with what you have, then prompt for missing info via email/Slack.
# Progressive data collection
def handle_incomplete_data(extracted: Dict, validation: Dict) -> Dict:
"""Generate follow-up requests for missing data"""
missing = validation.get('missing_fields', [])
if not missing:
return {'status': 'complete'}
HRIS API Rate Limits
Implement request queuing with exponential backoff. Use batch APIs where available.
# Rate-limited HRIS sync with queue
import asyncio
from asyncio import Queue
import time
class RateLimitedHRIS:
def __init__(self, requests_per_minute: int = 60):
self.rpm = requests_per_minuteSurvey Fatigue & Response Rates
Use AI to analyze open-ended responses, not just ratings. Send personalized follow-ups. Gamify with leaderboards for teams with highest response rates.
# Analyze survey sentiment and generate follow-ups
from anthropic import Anthropic
async def analyze_survey_response(response_text: str, employee_name: str) -> Dict:
"""Analyze sentiment and generate personalized follow-up"""
anthropic = Anthropic(api_key=os.getenv('ANTHROPIC_API_KEY'))
prompt = f"""Analyze this onboarding survey response and:Adjust Your Numbers
❌ Manual Process
✅ AI-Automated
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