Domain-Specific LLM Fine-Tuning: Accuracy + Cost + Compliance

Domain-Specific LLM Fine-Tuning: Accuracy + Cost + Compliance** The era of "one LLM for all tasks" is ending. Domain-specific fine-tuning is emerging as the path to higher accuracy, lower costs, and regulatory compliance. **Evidence:** - **FinLoRA benchmarking:** 40.1-point average performance gain over base models on financial NLP tasks (SEC filing analysis, financial sentiment analysis, etc.).[19] - **BloombergGPT:** Domain-optimized finance NLP showing strong performance on both finance-specific and general tasks.[28] - **McKinsey data:** Financial institutions using domain-specific models for fraud detection reported 30% fraud reduction vs. general-purpose AI.[29] - **Technical innovation:** LoRA (Low-Rank Adaptation) and QLoRA enabling cost-effective fine-tuning on resource-constrained environments; enterprise teams can fine-tune open-source models (LLaMA, Mistral, Qwen) for in-house use.[30] **Why This Matters:** 1. **Compliance:** Smaller, domain-specific models are explainable, auditable, and suitable for regulated use cases; general-purpose LLMs raise red flags with regulators.[18] 2. **Cost:** Domain-specific models are typically 10–100x smaller than general-purpose models, reducing inference costs and improving latency. 3. **Accuracy:** Purpose-built models outperform general models on domain-specific tasks by 15–40% on many benchmarks. **Strategic Recommendation:** - **Q1 2026:** Pilot LoRA fine-tuning on 2–3 in-house use cases (e.g., fraud scoring, regulatory change detection, customer intent classification). - **Q2–Q3 2026:** Build internal fine-tuning pipeline; train teams on parameter-efficient tuning (LoRA, QLoRA, adapter tuning). - **Investment:** Low-Medium ($1–5M; compute costs low if using open-source models and LoRA). - **Time horizon:** 6–12 months to production; quick wins are achievable. *** ## Tier-3 Considerations (Medium-Signal, Longer Horizon)