ChatGPT demonstrated to the world that large language models can do remarkable things. But for enterprise applications — legal contract analysis, medical record summarisation, financial statement parsing — the general-purpose models often fall short. In 2025, the frontier of enterprise NLP is domain-specific fine-tuning, and the performance gaps are larger than most people realise.
Where General LLMs Struggle
General LLMs are trained to be helpful across all domains, which means they're optimised for breadth rather than depth. For enterprise document processing, this creates three problems: (1) Hallucination rates are higher on domain-specific terminology. (2) Models lack knowledge of proprietary formats, internal nomenclature, and domain conventions. (3) Output formatting is inconsistent — enterprise workflows need structured, predictable outputs, not conversational prose.
The Fine-Tuning Advantage
When we fine-tuned a 7B parameter base model on a client's legal contract dataset — approximately 180,000 annotated contracts — and compared it against GPT-4 on their specific extraction tasks, the fine-tuned model won on every dimension: 97.3% accuracy vs 89.1%, 8× lower inference cost, consistent structured output without prompt engineering, and 5× lower latency. This pattern holds across every domain where we've done systematic evaluation.
The LoRA Revolution
Fine-tuning used to require massive compute budgets. Low-Rank Adaptation (LoRA) and its variants (QLoRA, DoRA) have democratised fine-tuning by reducing the number of trainable parameters by 99%+ while maintaining most of the performance benefit. A domain-specific fine-tune that would have cost $50,000 in compute two years ago now costs under $500. This economics shift is why fine-tuned models are moving from research curiosity to enterprise standard.
Beyond Classification: Structured Extraction at Scale
The most valuable enterprise NLP use case in 2025 is not sentiment analysis or classification — it's structured data extraction from unstructured documents. Extracting specific fields from contracts, pulling key metrics from earnings reports, parsing medical records into structured schemas. Fine-tuned models with constrained decoding (JSON mode, function calling) achieve extraction accuracy in the high 90s — high enough to automate workflows that previously required human review.
Our Recommendations
For enterprises evaluating NLP solutions in 2025: (1) Benchmark domain-specific fine-tunes against general models on your actual data before choosing an approach. (2) Invest in annotation infrastructure — the quality of your training data is the ceiling on model performance. (3) Plan for model versioning from day one — fine-tuned models need retraining as your domain evolves. (4) Use open-weight base models (Mistral, Llama, Phi) where data privacy is a concern — keeping your proprietary data on-premises is often a non-negotiable.