Learn how fine-tuning customizes AI models using real business data that improves accuracy, tone, and performance for voice agent conversations.
AI Model Fine-Tuning is the process of adapting a pre-trained large language model (LLM) to your specific business use case by training it on custom datasets—like real conversations, knowledge bases, and brand-specific vocabulary.
Think of it as narrowing the AI’s focus. Instead of asking it to handle every possible type of conversation, you’re teaching it to perform exceptionally well in the conversations your business actually has—whether that’s healthcare support, SaaS onboarding, or insurance claims.
Generic AI agents may speak fluently, but they don’t understand your customers, your product, or your tone. Without fine-tuning, their responses can feel off-brand, incomplete, or irrelevant.
Fine-tuned models help your AI voice agents:
Sound Like Your Team: Align with your brand’s tone and language.
Understand Nuance: Catch subtle cues, industry terms, or policy references.
Reduce Errors: Minimize hallucinations and irrelevant replies.
Collect Domain-Specific Data
Use historical support calls, CRM notes, or help center content that reflects real interactions.
Structure and Annotate
Tag examples by category (intent, sentiment, resolution type) to make the training process more effective.
Train the Model
Use machine learning pipelines to fine-tune the model’s weights based on your data and desired behaviors.
Evaluate and Retrain
Use call testing, feedback scores, and human review to continuously refine the model’s performance.
A real estate firm trains their voice agent on thousands of lead qualification calls. The fine-tuned model learns how to ask the right questions, handle objections, and hand off hot leads directly to a human rep with all context preloaded.
Training Data
Large Language Model (LLM)
Prompt Engineering
Human-in-the-Loop (HITL)
Revolutionize your call operation with Retell.
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