Toward scalable personalization of language models

Summary

Large Language Models (LLMs) have reached enterprise scale, but not enterprise relevance. While models like GPT, LLaMA, and Mistral excel at general reasoning, they fall short when applied to specialized domains or individual user contexts.

This article outlines a two-stage fine-tuning strategy that enables organizations to deploy domain-aware and user-personalized AI at scale, without prohibitive cost or complexity. The approach separates domain specialization from user personalization, creating a modular, efficient pathway to high-impact AI systems.

The enterprise challenge

Most LLMs are trained on broad, generic data. This creates four structural challenges for enterprises:

• Low relevance for domain-specific workflows

• Poor personalization with sparse or noisy user data

• High costs when fine-tuning models repeatedly

• Limited reuse across teams, clients, or business units

Directly fine-tuning a base model for every user or use case is neither scalable nor economical.

The two-stage solution

Stage 1: Domain adaptation

First, adapt a general-purpose LLM to a specific business domain—such as finance, healthcare, retail, or logistics.

Outcome:
A reusable domain model that understands industry language, rules, and workflows.

Why it matters:

• One-time investment

• Reusable across teams, customers, and products

• Creates a strong foundation for downstream personalization

Stage 2: User personalization

Next, fine-tune the domain-adapted model using lightweight user-specific data, preferences, interaction history, constraints, or portfolios.

Outcome:
A personalized model that delivers contextually accurate, user-aligned outputs.

Why it matters:

• Faster adaptation with less data

• Lower compute cost

• Higher relevance and satisfaction

Why this works

Separating domain learning from user learning creates a clear performance and cost advantage:

Personalized model > Domain model > Base model

In practice, this means:

• Better task accuracy

• Faster time-to-value

• Sustainable scaling across thousands of users

High-impact business use cases

• Financial services
  Domain-trained models on regulations and markets, personalized to individual portfolios and risk profiles.

• Retail and fashion
  Trend-aware models customized for specific brands, designers, or customer segments.

• Logistics and supply chain
  Routing and optimization models tailored to local constraints and operating conditions.

• Enterprise technology
  Internal copilots are adapted to company documentation, tools, and user behavior.

• Healthcare
  Clinical models specialized by medical domain and personalized to patient data (with appropriate governance).

Making it scalable in practice

To deploy this approach efficiently, leading organizations combine it with:

Parameter-efficient fine-tuning

Techniques like LoRA, QLoRA, and adapters drastically reduce training costs by updating only small model components.

Continual learning

Models evolve as user data changes, without losing prior knowledge, ensuring relevance over time.

Meta-Learning

Few-shot learning enables rapid onboarding of new users or clients with minimal data.

Enterprise-grade infrastructure

Distributed training frameworks, optimized data pipelines, and GPU/TPU clusters ensure production readiness.

Takeaways

The future of enterprise AI is not one large model per company, nor one model per user, but a layered architecture that balances scale with relevance.

By decoupling domain intelligence from user personalization, organizations can:

• Reduce AI deployment costs

• Accelerate time-to-impact

• Reuse capabilities across the enterprise

• Deliver truly differentiated, personalized experiences

This two-stage fine-tuning framework represents a practical step toward operationalizing LLMs as strategic, scalable business assets rather than experimental tools.