AI Engineering
Enterprise LLM Fine-Tuning Services
We fine-tune large language models on your domain data to create AI systems that truly understand your business. From medical terminology to legal reasoning, financial analysis to technical documentation — fine-tuned models deliver specialized accuracy that generic LLMs cannot match.
What Is LLM Fine-Tuning?
LLM fine-tuning is the process of further training a pre-trained large language model on your domain-specific data to improve its performance on specialized tasks. Unlike RAG (which retrieves context at query time), fine-tuning modifies the model's internal weights so it inherently understands your domain terminology, reasoning patterns, and desired output style.
Think of it as the difference between giving someone a reference book (RAG) versus teaching them the subject (fine-tuning). Both are valuable — and many production systems use both together for maximum performance.
When to Fine-Tune an LLM
- Domain-specific reasoning — legal analysis, medical coding, financial modeling
- Consistent output format — structured JSON, specific report templates, code generation
- Style and tone — matching your brand voice, technical writing standards
- Reduced latency — smaller fine-tuned models can outperform larger generic models
- Cost optimization — a fine-tuned 7B model can match GPT-4 on narrow tasks at 1/100th the cost
What We Deliver
Fine-Tuning Capabilities
Data Preparation
Training data curation, cleaning, deduplication, and augmentation. We transform your raw documents into high-quality instruction-response pairs optimized for fine-tuning.
Supervised Fine-Tuning (SFT)
Full fine-tuning and parameter-efficient methods (LoRA, QLoRA) for instruction following, classification, extraction, and generation tasks on your domain data.
RLHF & DPO Alignment
Reinforcement learning from human feedback and direct preference optimization to align model outputs with your quality standards and safety requirements.
Model Evaluation
Comprehensive evaluation frameworks measuring accuracy, faithfulness, toxicity, bias, and domain-specific metrics. Automated regression testing for production models.
Model Serving & Deployment
Optimized inference deployment with quantization (GPTQ, AWQ), vLLM serving, auto-scaling, and A/B testing. Cloud, on-premise, or edge deployment.
Continuous Improvement
Production feedback loops that capture model successes and failures, enabling iterative fine-tuning cycles that improve performance over time.
Technology
Fine-Tuning Tech Stack
FAQ
LLM Fine-Tuning Questions
What is LLM fine-tuning?
LLM fine-tuning is the process of further training a pre-trained large language model on your domain-specific data to improve its performance on specialized tasks. Unlike RAG, which retrieves context at query time, fine-tuning modifies the model's weights so it inherently understands your domain terminology, reasoning patterns, and output style.
When should I fine-tune vs use RAG?
Fine-tune when you need specialized reasoning patterns, consistent output format, domain-specific language, or reduced latency. Use RAG when you need frequently updated data, citation-backed responses, or auditability. Many production systems use both. Read our detailed comparison.
How much training data do I need?
Quality matters more than quantity. For task-specific fine-tuning, 500-5,000 high-quality examples can produce significant improvements. For deep domain adaptation, 10,000-50,000+ examples are typical. We help curate and validate training data to ensure quality.
How much does LLM fine-tuning cost?
Enterprise LLM fine-tuning projects typically range from $40,000-$150,000+ including data preparation, model training, evaluation, and deployment. Fine-tuning open-source models (Llama, Mistral) is significantly cheaper than fine-tuning via proprietary APIs. Contact us for a detailed estimate.
Ready to Fine-Tune Your LLM?
Tell us about your domain and use case — we'll evaluate whether fine-tuning, RAG, or a hybrid approach is right for you.
Get Free Consultation