How to Fine-Tune Your Own Model on ResponseCX

This post outlines what may become one of the most powerful operational flywheels in modern enterprise software and introduces a new, transformative role: the AI Agent Trainer.

For years, we’ve asked: What if every customer experience could be autonomous, instant, and unmistakably delightful? What would it take to build the next-generation software company powered by an autonomous operating system—one that is continuously improving, fully agentic, and deeply integrated into the workflows of the world’s fastest-growing brands?

That future is no longer hypothetical. It’s here in practice, not just in theory.

By leveraging real customer interactions and synthetic data pipelines, human labeled preferred vs. non-preferred outputs and applying advanced techniques like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), organizations can now train and refine their AI agents.

This capability is no longer limited to ML researchers or specialized engineering teams. It marks the next frontier in customer experience and commerce operations and it’s already reshaping how the most innovative companies operate.

The Last 10% Problem in AI Agents

Prompt techniques, knowledge based RAG, rules, brand tone of voice attributes and macros get you ~90% of the way there. But the last 10% matters immensely in customer support, order processing, recommendation systems and other operational workflows.

✅ The agent must execute the right function at the right time.
✅ It must follow guardrails; no giveaways, no medical advice, no hallucinations.
✅ It must sound human with direct and informative responses
✅ And most importantly, it must learn from feedback.

Most organizations try to close this gap by throwing more prompts, more logic, or more RAG complexity at the problem. But there are diminishing returns. It works but it’s reactive and slow. It requires engineers and blurs the line between product and support.

Introducing: The AI Agent Trainer Workflow

What’s needed is a repeatable flywheel:

1. Review responses and provide preferred or non-preferred outputs.

2. Score them with consistent evals (accuracy, tone, completion, etc.).

3. Export them to create fine-tuning synthetic datasets.

4. Train the model to improve based on real, nuanced customer interactions.

The agent learns just like a human would. And now L1 support teams are active in the process for truly training the AI Agent.

Three Massive Unlocks

1. Separation of Setup and Improvement

Setup = knowledge base, functions, rules, brand attributes.
Improvement = structured feedback, evals, datasets and model tuning.

This shift moves improvement out of engineering and into a data-driven QA loop.

2. Solving for Low-Volume Use Cases

Brands often start with narrow support workflows. Fine-tuning lets you drive high-quality outcomes even with limited data; often just 50-100 examples of conversational outputs can be enough to begin SFT & DPO based finetuning.

3. Cost-Effective Scaling

Smaller, distilled fine-tuned models are faster, cheaper, and more accurate. This path dramatically reduces reliance on large foundation models.

How to Fine-Tune Your Own AI Agent with ResponseCX

At StateSet, we’ve built all of this into ResponseCX. Here’s how any team can go from zero to a fine-tuned model seven steps:

1. Create Your AI Agent

Start by creating your base AI Agent inside the ResponseCX platform.

You’ll define the foundation:

• Function calls it can execute

• Knowledge, rules and macros it can reference

• Guardrails for compliance and safety

This is your AI Agent’s initial brain—set it up right, and you’re already 90% there.

2. Generate Responses in QA Mode

Next, simulate real-world customer scenarios directly from ResponseCX or from your helpdesk. Capturing what the output would be and being able to create evals directly from the interface.

Test your agent across different:

• Intents

• Edge cases

• Phrasings and variations

Capture these raw outputs. These aren’t just test cases—they’re future training data.

3. Create Evaluations (Evals)

Here’s where the magic happens.

Each response should be reviewed and:

• Scored on dimensions like accuracy, tone, and follow-through

• Labeled and evaluation with the preferred or non-preferred outputs

• Optionally include comments on what was right or wrong

This is human-in-the-loop feedback—critical for teaching your agent what “good” actually looks like.

4. Export Evals to a JSONL File

Once your evaluations are complete, export them into a DPO-ready .jsonl format directly from the ResponseCX dashboard.

Each record will include:

• The prompt (customer message)

• The chosen response (preferred)

• The rejected response (non-preferred)

This structured format is optimized for fine-tuning using modern DPO preference-based techniques.

5. Import Your Dataset

Upload your exported JSONL file to the Files interface in ResponseCX.

You can preview the dataset, confirm structure, and optionally filter or tag examples by use case or topic.

6. Launch the Fine-Tuning Job

Select:

• The base model you want to fine-tune

• Your uploaded dataset

• Hyperparameters (or use recommended defaults)

Then hit Run which will automatically:

• Train your new model using the selected dataset

• Apply DPO and/or SFT depending on your configuration

• Version your model for traceability

7. Use Your New Fine-Tuned Model

Once training is complete, simply assign the new model version to your Agent & test it directly from the ResponseCX interface.

Now you have:

• A smarter, more accurate AI Agent

• Fined-tuned datasets to real customer interactions

• Continuously getting better over time

What You’ve Just Built

You didn’t just fine-tune a model. You created a learning system.
A flywheel that gets smarter with every message, every feedback, every eval.

This is how AI Agents go from “helpful” to enterprise-grade.

This is how support becomes proactive.
This is a new profession of the future: AI Agent Trainer.

Let’s put the flywheel in motion.

👉 Reach out to get started: [email protected]