LLM Fine-tuning for Domain-Specific Enterprise Applications

When to Fine-tune vs. Prompt Engineer

The decision between prompt engineering and fine-tuning depends on three factors: consistency requirements, volume, and knowledge depth. Prompt engineering works well for one-off queries but degrades in consistency at scale. Fine-tuning is the right choice when you need deterministic behavior, deep domain vocabulary, or proprietary knowledge baked into the model weights.

• Prompt engineering: flexible, low cost, variable output
• RAG: best for factual recall and up-to-date knowledge
• Fine-tuning: best for style, format, and behavioral consistency
• Combination: fine-tune + RAG for maximum capability

Dataset Construction

The quality of a fine-tuned model is entirely determined by the quality of its training data. We use a systematic approach: start with existing high-quality examples from the enterprise knowledge base, augment with synthetic data generated by a larger teacher model, and filter rigorously using embedding-based deduplication and quality classifiers.

Training Infrastructure

For most enterprise fine-tuning tasks, parameter-efficient fine-tuning (PEFT) methods — particularly LoRA and QLoRA — offer an excellent tradeoff between capability improvement and computational cost. A well-structured LoRA fine-tune on a 7B model can match GPT-4 performance on narrow domain tasks at a fraction of the inference cost.

Evaluation and Alignment

Evaluating fine-tuned models for enterprise use requires going beyond perplexity and BLEU scores. We build custom evaluation sets that test for domain accuracy, refusal behavior, format compliance, and latency under load. RLHF-style preference data collected from domain experts is used for final alignment.