Overview
This section covers how to train your AI agents for production-ready performance.
Why Training Matters
Raw LLMs are general-purpose. Your agents need to be experts in your specific domain, speak with your brand voice, and handle your edge cases gracefully.
Adaptive provides a complete training pipeline:
Synthetic Conversation Generation - Create training data automatically
Human Review & Annotation - Refine and correct agent behavior
Meta-Agent Scoring - Automated quality assessment
Fine-Tuning - Create custom models for your use case
Training Methods
Synthetic Conversations
Generate realistic training conversations that cover:
Common user queries
Edge cases and failure modes
Escalation scenarios
Multi-turn interactions
Real-Time Trainer
Interactive voice sessions where you can:
Talk to your agent in real-time
Provide immediate feedback
Iterate rapidly on responses
Auto Trainer
Automated training pipeline that:
Generates conversations at scale
Scores responses with meta-agents
Identifies problem patterns
Suggests improvements
Fine-Tuning
One-click integration for model fine-tuning:
Export training data in standard formats
Track fine-tuning jobs
Version control trained models
A/B test model performance
Pages in This Section
Synthetic Conversations - Generate training data
Real-Time Trainer - Interactive voice training
Auto Trainer - Automated training pipeline
Fine-Tuning - Create custom models
Meta-Agent Scoring - Quality assessment
Best Practices
Generate Diverse Data: Cover happy paths and edge cases
Review Before Fine-Tuning: Bad training data = bad model
Start with Base Models: Fine-tune only when needed
A/B Test: Compare fine-tuned vs base models
Iterate Continuously: Training is ongoing, not one-time
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