Introduction
Every query your enterprise AI application processes, every correction a subject matter expert makes — that interaction is valuable training data. Yet most organizations let this continuous signal go to waste. Empromptu AI's Alchemy Models platform changes that by automatically capturing validated outputs from production workflows and feeding them back into a fine-tuning pipeline. This guide walks you through the process of setting up a system that trains custom AI models directly from your running applications — no dedicated ML team required. You'll learn how to turn everyday corrections into model improvements, own your model weights, and reduce inference costs over time.
What You Need
- An existing enterprise AI application that processes queries or generates outputs (e.g., a customer support chatbot, document summarizer, or data extractor).
- Subject matter experts (SMEs) within your organization who can review and correct AI outputs.
- Access to Empromptu's Alchemy Models platform (including Golden Data Pipelines infrastructure).
- Structured enterprise data (cleaned and prepared) to prime the initial pipeline — this can come from your existing databases, APIs, or data lakes.
- Basic familiarity with the term “fine-tuning” — but no machine learning expertise is necessary.
Step-by-Step Guide
Step 1: Prepare Your Data Foundation
Before your AI application goes live, you need to clean, extract, and enrich your enterprise data. Empromptu's Golden Data Pipelines automate this step. Start by identifying the datasets your application will use — for example, historical customer conversations, product documentation, or compliance records. Run these through the pipeline to ensure they are structured, deduplicated, and enriched with relevant metadata. This foundation ensures your application starts with high-quality inputs, which directly impacts the quality of training data generated later.
Step 2: Build or Connect Your AI Application
With clean data in place, deploy your AI application using Empromptu's platform. The application can be a retrieval-augmented generation (RAG) chatbot, an automated document processor, or any other workflow that processes user queries. The key requirement is that the application produces outputs that subject matter experts can review and correct. For example, a customer support chatbot might generate answers that agents then validate. The platform automatically hooks into your application's output stream, so no additional instrumentation is needed.
Step 3: Enable Automatic Feedback Capture
Once your application is running, configure the Golden Data Pipelines to capture every output. This works in two stages: before the app is built (data preparation) and after it's running (feedback loop). The pipeline collects all outputs and routes them to a review queue. SMEs inside your organization access this queue through a simple interface (e.g., a dashboard or integrated into their existing tools). They mark each output as correct or provide corrections. The platform automatically logs these corrections as validated training data.
Step 4: Subject Matter Experts Review and Correct Outputs
This step is where the real training data is generated. SMEs review the outputs from your AI application and either approve them or correct errors. For instance, if an AI-generated summary misstates a metric, the SME corrects it. Each correction becomes a training example that teaches the model what the correct behavior should be. Empromptu's platform handles the labeling and structuring automatically — no manual data science work required. The more reviews you do, the richer your training dataset becomes.
Step 5: Automatic Fine-Tuning
After a sufficient number of validated outputs have accumulated (e.g., hundreds or thousands of examples), the platform triggers a fine-tuning run. It uses the validated corrections to adjust the model's weights, creating what Empromptu calls an Expert Nano Model — a small, task-specific model optimized for your particular workflow. Fine-tuning happens in the background without disrupting your production application. The platform also runs evals, guardrails, and compliance checks during this process, ensuring governance is built in.
Step 6: Deploy and Own Your Optimized Model
Once fine-tuning is complete, you receive the model weights. You own them outright. Empromptu hosts and runs inference on its infrastructure, but the weights are portable and exportable — you can run them elsewhere if needed. Deploy the updated model back into your production application, and the cycle repeats: new outputs are reviewed, more corrections are captured, and the model improves continuously. Over time, inference costs decrease because the model becomes more efficient at handling your specific tasks.
Conclusion Tips
- Start small: Pick a single, high-impact workflow to pilot (e.g., a customer support bot handling refund queries). This minimizes risk and proves the concept before scaling.
- Involve SMEs early: They are the key data generators. Make the review process as lightweight as possible — integrate it into their daily workflow, not an extra task.
- Monitor quality: Track how often SME corrections occur. A high correction rate may indicate the base model needs better initial fine-tuning or that your data foundation is weak.
- Plan for governance: Alchemy's pipeline includes evals and guardrails. Review these regularly to ensure your model remains compliant with internal policies and regulations.
- Scale gradually: Once the first model shows improvement, expand to other workflows. Each workflow becomes its own Expert Nano Model, and you can combine them as needed.
- Think long-term: The continuous feedback loop means your model gets better over months, not days. Be patient — the compounding effect of thousands of small corrections is powerful.
To learn more about the underlying technology, explore Empromptu's data preparation and fine-tuning pipeline. If you're ready to get started, contact Empromptu for access to the Alchemy Models platform.