Production AISystems
Engineering Capability

LLM Fine-Tuning and Model Adaptation

LLM fine-tuning that adapts language models to your domain, output format and reasoning style. Dataset preparation, instruction tuning, LoRA and QLoRA, evaluation with regression checks and adapter deployment. Know when fine-tuning is the right choice—and when RAG or prompt engineering is better.

Assess Whether Fine-Tuning Fits
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Engineering Decisions This Capability Addresses

Full vs parameter-efficient

Full fine-tuning modifies all weights. LoRA trains a small adapter—cheaper, swappable, but less expressive.

Dataset size and quality

500 high-quality examples often outperform 5,000 noisy ones. Quality and diversity matter more than volume.

Regression risk

Fine-tuning can improve the target task but degrade general capability. Regression checks are mandatory.

RAG vs fine-tuning

RAG for knowledge that changes. Fine-tuning for style, format and reasoning. Combined approach for both.

Reference Architecture and Workflow

1

Use case assessment

Determine whether fine-tuning is the right approach

control
2

Dataset preparation

Training data collected, cleaned, formatted and quality-checked

data
3

Instruction tuning

Model fine-tuned using LoRA or QLoRA

ai
4

Evaluation

Task performance, regression and hallucination rate measured

control
5

Regression check

General capability tested to detect catastrophic forgetting

control
6

Adapter deployment

Fine-tuned adapter deployed alongside base model

output
7

Monitoring

Model performance monitored in production. Drift triggers retraining

feedback

Options and Trade-offs

Instruction tuning

Fine-tune to follow specific instructions and output format consistently.

Domain adaptation

Fine-tune on domain-specific data for terminology and reasoning patterns.

LoRA and QLoRA

Parameter-efficient. Trains a small adapter. Lower cost, swappable.

Combined RAG and fine-tuning

Fine-tune for style and format, use RAG for knowledge. Often the best approach.

Evaluation, Operational Controls and Failure Handling

Task score

How well the fine-tuned model performs on the target task compared to the base

Win rate

Percentage of cases where the fine-tuned output is preferred over the base model's

Regression rate

Percentage of general tasks where the fine-tuned model performs worse than the base

Hallucination rate

How often the fine-tuned model produces unsupported claims

Inference impact

Whether the fine-tuned model is slower or more expensive to serve than the base

Solutions That Use This Capability

AI Chatbot Solutions

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Conversational AI Solutions

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Enterprise AI Search

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Assess Whether Fine-Tuning Fits

Tell us the engineering challenge you are facing. We respond with how we would approach it.

Assess Whether Fine-Tuning Fits

No finished technical specification required.

Frequently Asked Questions

When should I fine-tune instead of using RAG?
Fine-tune when the model needs to learn a specific style, format or reasoning pattern that prompt engineering cannot achieve reliably. Use RAG when the model needs access to knowledge that changes frequently or requires citations. They are not mutually exclusive—fine-tune for style and format, use RAG for knowledge. The combined approach is often the best for production systems.
What is LoRA and why is it useful?
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that trains a small adapter—a few million parameters—instead of modifying the full model. This reduces training cost, enables swappable adapters for different use cases and allows a single base model to serve multiple fine-tuned configurations. QLoRA adds quantisation for even lower cost.
Can fine-tuning cause the model to lose general capability?
Yes—this is called catastrophic forgetting. The model can become better at the target task but worse at general tasks. Regression checks test the fine-tuned model on general benchmarks to detect this. If regression is significant, the training approach is adjusted—lower learning rate, fewer steps, or a combined approach that preserves general capability.
How much data do you need for fine-tuning?
It depends on the task. Instruction tuning typically needs 500 to 5,000 high-quality examples. Quality matters more than quantity—1,000 well-formatted, diverse examples outperform 10,000 noisy ones. The dataset is reviewed for quality, diversity and bias before training. Data preparation is a significant part of the engagement.