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

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