team training
**Building AI Team Capabilities**
**Training Program Structure**
**Tier 1: AI Literacy (Everyone)**
**Duration**: 2-4 hours
**Audience**: All employees
Topics:
- What are LLMs and how do they work?
- When to use AI vs traditional solutions
- Prompt engineering basics
- AI safety and responsible use
**Tier 2: AI Practitioner (Technical Teams)**
**Duration**: 1-2 days
**Audience**: Developers, data scientists
Topics:
- API integration patterns
- Fine-tuning fundamentals
- RAG architecture
- Testing and evaluation
- Cost optimization
**Tier 3: AI Specialist (AI Team)**
**Duration**: Ongoing
**Audience**: ML engineers
Topics:
- Model architecture deep dives
- Training infrastructure
- Deployment and scaling
- Research paper reviews
**Internal Playbook Components**
**1. Decision Framework**
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**2. Model Selection Guide**
| Use Case | Recommended Model | Fallback |
|----------|-------------------|----------|
| Simple chat | GPT-3.5/Claude Haiku | Llama-8B local |
| Complex reasoning | GPT-4/Claude Opus | Llama-70B |
| Code generation | Claude/GPT-4 | CodeLlama |
| High volume | Fine-tuned small LLM | GPT-3.5 |
**3. Prompt Templates**
Standardized templates for common tasks:
- Customer support responses
- Code review suggestions
- Document summarization
- Data extraction
**4. Security Guidelines**
- Never send PII to external APIs without anonymization
- Use internal models for sensitive data
- Audit logs for compliance
- Regular security reviews
**Measuring Training Effectiveness**
| Metric | Target |
|--------|--------|
| Training completion | >90% |
| Prompt quality scores | Improve 30% |
| AI adoption rate | Increase 50% |
| Error/incident rate | Decrease 40% |
**Resources for Teams**
- Internal AI documentation wiki
- Slack channel for AI questions
- Office hours with AI team
- Example code repositories
- Case studies and success stories