2. Identifying the Real Use-Case for Customisation

From my experience, companies often jump into fine-tuning before defining what they actually want the model to do. In reality, the process starts with a clear problem statement.

Ask questions like:

• What exact task do we want AI to perform?
  (classification, summarization, generation, code review, decision support, etc.)

• Where is the model currently failing?
  (hallucinations, lack of domain understanding, accuracy issues)

• What domain-specific behaviours are required?
  (legal compliance, medical terminology, product catalog knowledge)

• Will customising the model significantly reduce manual effort or errors?

When these questions are answered, we identify whether the business needs:

• Prompt optimisation

• RAG (for knowledge-heavy cases)

• PEFT/fine-tuning (for behaviour-specific training)

3. Preparing and Structuring Domain Data for Training

Data is the single most critical element in AI customisation. The model performance depends heavily on:

• Quality of data

• Consistency

• Domain-specific coverage

• Instruction format

• Noise removal

Based on projects we’ve worked on, the most effective dataset formats include:

a) Instruction–Response Pairs

b) Conversation Logs

Used for AI agents, chatbots, support systems.

c) Task Demonstrations

d) Domain Knowledge Documents

Policies, manuals, product guides converted into training chunks.

e) Synthetic Data

Sometimes generated using a larger model under expert supervision.

Practical Insight:
For most enterprise fine-tuning, 2,000–20,000 well-curated examples are enough to get high-quality results.
In comparison, general LLMs are trained on trillions of tokens — you don’t need that scale.

4. Choosing the Right Model for Customisation

Model selection depends on:

• Use-case type

• Regulatory requirements

• Data privacy constraints

• Deployment preference (cloud vs on-premise)

Common choices:

Open-source Models

Llama 3, Mistral, Falcon, Gemma
✔ Full control
✔ Can be tuned locally
✔ Best for privacy-sensitive sectors

Closed-source Models

OpenAI GPT-4.1, Gemini 1.5, Claude 3.5
✔ Higher reasoning abilities
✔ Zero maintenance
✔ Fine-tuning (for some models) can be expensive

Small/Custom Models

Phi, LLaMA 3.1 8B, Mistral 7B
✔ Lightweight
✔ Perfect for mobile/on-device use

In 2025, most companies prefer 8B–70B parameter open-source models for fine-tuning to maintain ownership of data behaviour.

5. Implementing the Fine-Tuning or Customisation Workflow

Below is the practical, step-by-step process we follow in real projects:

Step 1: Define Custom Behaviour

Examples:

• “The model should talk like our brand.”

• “The model should use internal product terminology.”

• “The model should never hallucinate financial data.”

• “The model should recommend X based only on approved knowledge.”

Step 2: Build or Collect the Dataset

This includes:

• Formatting data into training pairs

• Validating examples manually

• Removing noise & duplicates

• Balancing examples across categories

• Annotating special instructions

Step 3: Choose the Customisation Method

For accurate domain answers → RAG

For changing model behaviour → PEFT

For completely new tasks → Full Fine-tuning

Step 4: Train with Evaluation Loops

We use:

• QLoRA / LoRA for efficient training

• Early stopping to avoid overfitting

• Mixed precision training (FP16, BF16)

• 3-level evaluation (human, automated, domain-expert)

Benchmark:
In a typical enterprise setting, fine-tuning an 8B model with LoRA takes 3–6 hours on a modern GPU machine.

Step 5: Integrate the Model with Deployment Architecture

Depending on requirements:

• API deployment (FastAPI, Node.js, .NET)

• On-device inference for field apps

• Multi-agent orchestration

• Cloud platforms (AWS Sagemaker, Azure, GCP)

• Vector DB integration (for RAG models)

We ensure:

• High throughput

• Low latency

• Scalable inference

• Monitoring for drift and behaviour changes

Step 6: Continuous Evaluation & Improvement

Post-deployment, we monitor:

• Accuracy degradation

• User feedback loops

• Mistake logging

• Domain-specific hallucinations

• Content safety issues

We then schedule periodic improvement cycles (usually every 3–6 months).

6. Practical Challenges and How We Solve Them

Over time, we’ve encountered several real-world constraints:

Challenge 1: Not Enough Training Data

Solution:
Generate synthetic training data under expert supervision.

Challenge 2: Hallucinations in Domain Cases

Solution:
Use RAG with strict citation enforcement.

Challenge 3: High Fine-Tuning Cost

Solution:
Use PEFT/QLoRA and smaller models that can outperform larger untuned models.

Challenge 4: Keeping Data Updated

Solution:
Continuous RAG indexing + periodic mini-tuning.

Challenge 5: Model Overfitting on Narrow Data

Solution:
Mix general training examples with domain examples (30:70 ratio works well).

7. When Should You Fine-Tune vs Use RAG?

Most companies begin with RAG and add fine-tuning only when behaviour needs to change deeply.

8. Expected Business Impact (Based on Real Implementation Stats)

From the implementations we have done across healthcare, SaaS, finance, HR tech, and ecommerce:

• 70–85% reduction in manual workload for domain-specific tasks

• 40–60% improvement in output accuracy vs general models

• 50–75% cost savings using PEFT instead of full fine-tuning

• Up to 90% increase in response reliability in regulated domains

• 2–4x faster deployment cycles with reusable training pipelines

These numbers clearly show that domain-specific fine-tuning is not just a technical improvement—it is a business growth accelerator.

FAQs 

1. What is the best way to customise a generative AI model for a specific domain?

The best approach depends on the use case—RAG is best for domain knowledge, while PEFT or fine-tuning is better when changing the model’s behaviour or tone.

2. How much training data is needed for fine-tuning?

A typical enterprise fine-tuning task needs 2,000–20,000 high-quality examples, depending on complexity.

3. What is the difference between RAG and fine-tuning?

RAG adds external knowledge at inference time, while fine-tuning permanently changes how the model behaves based on training data.

4. How expensive is it to fine-tune a generative AI model?

Using methods like LoRA/QLoRA, costs can drop by 60–80%, and most 7B–13B models can be tuned on a single GPU system.

5. Why does my fine-tuned model still hallucinate sometimes?

Hallucinations typically occur due to data imbalance, insufficient domain examples, or absence of a RAG system. Adding knowledge retrieval or improving dataset quality usually resolves this.