Building a Production-Ready LLM Fine-Tuning Pipeline
How I built an end-to-end fine-tuning pipeline for GPT-4.1-mini that achieved 40% improvement in task-specific accuracy and 60% reduction in hallucinations.
Overview
Fine-tuning large language models for domain-specific tasks is challenging. You need high-quality data, robust preprocessing, careful hyperparameter tuning, and comprehensive evaluation. This article walks through how I built a complete pipeline that handles all of these challenges.
The goal was to create a repeatable, production-ready system that could fine-tune GPT-4.1-mini on domain-specific datasets while maintaining quality and measurable performance improvements. The result was a 40% boost in accuracy and 60% fewer hallucinations compared to the base model.
Pipeline Architecture
Data Collection
Curated 15K+ training examples from HuggingFace Datasets with domain-specific focus
- HuggingFace Datasets integration
- Domain-specific filtering
- Quality validation
Preprocessing
Built custom preprocessing pipeline ensuring data quality and proper JSONL formatting
- Text normalization
- Format validation
- Train/validation split
Fine-Tuning
Trained GPT-4.1-mini using OpenAI Platform with optimized hyperparameters
- Learning rate tuning
- Batch size optimization
- Multiple training runs
Evaluation
Implemented automated evaluation framework with BLEU, ROUGE, and custom metrics
- Automated benchmarking
- Metric tracking
- Performance analysis
Key Results
Deep Dive
1. Data Collection & Curation
The foundation of any successful fine-tuning project is high-quality data. I leveraged HuggingFace Datasets to access domain-specific datasets, then built custom filters to ensure relevance and quality.
The challenge wasn't just collecting data—it was ensuring that each example was properly structured, relevant to the domain, and free from noise. I implemented multi-stage filtering that checked for completeness, coherence, and domain alignment.
Key Insight: Quality over quantity. 15,000 high-quality examples outperformed 50,000 noisy examples in early experiments.
2. Preprocessing Pipeline
OpenAI's fine-tuning API requires data in a specific JSONL format. Each line contains a JSON object with messages in the chat format. I built a preprocessing pipeline using Python and Pandas that:
- Normalized text encoding and removed special characters
- Validated message structure and token counts
- Split data into training (80%) and validation (20%) sets
- Generated quality metrics and statistics for each batch
This preprocessing layer was crucial for maintaining consistency and catching issues before expensive training runs.
3. Fine-Tuning Process
Using the OpenAI Platform, I ran multiple training iterations with different hyperparameters. The key was finding the right balance between learning rate, batch size, and number of epochs.
I tracked each run using Weights & Biases, monitoring training loss, validation loss, and early stopping conditions. This systematic approach helped identify the optimal configuration without overfitting.
Optimal Configuration
Training & Validation Loss
The model converged smoothly over 3 epochs with minimal overfitting, as shown by the close tracking of training and validation loss.
4. Evaluation Framework
Measuring improvement required a comprehensive evaluation framework. I implemented automated benchmarking using:
- BLEU & ROUGE: Standard metrics for text generation quality
- Custom Domain Metrics: Task-specific accuracy measurements
- Hallucination Detection: Automated fact-checking against ground truth
This multi-metric approach provided a holistic view of model performance and helped identify specific areas for improvement.
Base vs Fine-Tuned Performance
Consistent improvement across all metrics, with particularly strong gains in task-specific accuracy and factual correctness.
Final Model Evaluation Metrics
All evaluation metrics exceeded 70%, with factual accuracy reaching 94%, demonstrating strong performance across the board.
Lessons Learned
Data quality trumps everything. Investing time upfront in curation and preprocessing paid massive dividends. A smaller, high-quality dataset consistently outperformed larger, noisier alternatives.
Systematic experimentation is crucial. Tracking every hyperparameter, metric, and decision made it possible to iterate quickly and understand what actually moved the needle.
Evaluation drives improvement. Without comprehensive metrics, it's impossible to know if changes are helping or hurting. The automated evaluation framework made it easy to test hypotheses and validate improvements.