Why LLM Customization Is Needed

General-purpose LLMs can handle diverse tasks, but they aren't optimized for specific domains or workflows. Using precise medical terminology, following a company's unique writing style, or consistently generating output in a specific format all require customization.

There are two main approaches: fine-tuning and prompting.

Fine-tuning

Fine-tuning updates the weights of a pre-trained model with additional data. Since it modifies the model itself, it performs the desired behavior without special prompts after training.

When Fine-tuning Is Appropriate

Consistent output format: Always producing the same JSON structure or specific report templates
Domain-specific terminology: Specialized fields like medical, legal, or financial
High-volume repetitive tasks: Processing thousands of identical task types
Latency minimization: Fast responses needed without long prompts
Long-term cost reduction: Reducing prompt token costs over time

Limitations of Fine-tuning

Time and cost for training data preparation (minimum hundreds to thousands of examples)
GPU resources required
Retraining needed when model updates
Risk of overfitting
Limited for injecting new knowledge (may increase hallucinations)

Prompting

Prompting guides desired behavior through input prompts without modifying the model. It leverages system prompts, few-shot examples, RAG, and more.

When Prompting Is Appropriate

Rapid experimentation: Test and iterate immediately
Diverse tasks: Performing multiple task types with a single model
Current information: Providing real-time information via RAG
Small-scale projects: Limited training data or investment capacity
Flexible changes: Only modify prompts when requirements change

Limitations of Prompting

Long prompts increase token costs
Context must be provided with every request
Complex prompt maintenance challenges
Consistency may be lower than fine-tuning

Comparison Table

Criterion	Fine-tuning	Prompting
Initial cost	High (data + GPU)	Low
Operating cost	Low (short prompts)	Medium to high (long prompts)
Implementation time	Days to weeks	Hours to days
Flexibility	Low	High
Consistency	High	Medium
Latest information	Retraining needed	Instant via RAG
Technical difficulty	High	Low to medium

Practical Decision Framework

Step 1: Start with Prompting

In most cases, prompting is sufficient. First check whether system prompts and few-shot examples can achieve the desired results.

Step 2: Add RAG

If domain knowledge is needed, try RAG before fine-tuning. Retrieving external documents as context satisfies most specialized domain requirements.

Step 3: Consider Fine-tuning

Consider fine-tuning when all of these conditions are met:

Prompting + RAG quality is insufficient
Sufficient training data available (500+ examples)
Repetitive, consistent task types
Cost/performance optimization is critical

Step 4: Hybrid Approach

Combining a fine-tuned model with RAG yields the best results. The model handles domain style and formatting, while RAG provides current factual information.

2026 Trends

Fine-tuning democratization: Lightweight techniques like LoRA and QLoRA have significantly lowered costs and barriers to entry
Prompt → Context Engineering: Evolution from simple prompts to full context design
Automatic optimization: AI automatically generating optimal prompts or fine-tuning data

Conclusion

Fine-tuning and prompting are not an either/or choice but a spectrum. For most projects, starting with prompting + RAG and gradually introducing fine-tuning as needed is the practical strategy. The most important principle is "try the simplest approach first."

AI Customization Posts

Fine-tuning vs Prompting: When Should You Choose Which?