Do I need to train an AI model for my business?

Probably not. Most business AI uses RAG (retrieval-augmented generation)—the AI accesses your documents in real-time without training. You only need custom training if: off-the-shelf models fail on your specific task (30%+ error rate), you have massive proprietary data to learn from, or you need a model that embeds deep domain knowledge.

How long does AI training take?

Fine-tuning existing models: 2-4 weeks including data prep. Training smaller custom models: 4-8 weeks. Full custom model training: 2-6 months (not recommended for most businesses). For most SMEs, fine-tuning or RAG is the right approach—you can skip full training entirely.

What's the difference between training and fine-tuning?

Training builds a model from scratch—expensive, slow, needs massive data. Fine-tuning adapts an existing model to your use case—faster, cheaper, works with less data. Most businesses fine-tune, they don't train. RAG is even simpler—just give the model access to your docs, no retraining needed.

How Long Does It Take to Train an AI Model for My Business?

Here's the truth most vendors won't tell you: you probably don't need to train an AI model at all.

Training vs RAG vs Fine-tuning

Approach	How It Works	Time	Cost
RAG (recommended)	AI reads your docs in real-time	Days	¥200k-500k
Fine-tuning	Adapt existing model	2-4 weeks	¥500k-2M
Full training	Build new model	2-6 months	¥10M+

Why Most Businesses Use RAG

RAG (Retrieval-Augmented Generation) is the modern approach:

No training needed: AI accesses your knowledge base
Instant updates: Add docs, AI knows immediately
Cheaper: No compute costs for training
Faster: Implement in days, not weeks
Transparent: You can see what docs AI used

When You Need Fine-tuning

Fine-tuning makes sense when:

Off-the-shelf models consistently fail your task (>30% error)
You need specific output formats always
Performance on your specific data is critical
You have quality training data available

Fine-tuning Timeline

Data preparation: 1-2 weeks (collect, clean, format examples)
Fine-tuning: 3-7 days (compute time)
Evaluation: 3-5 days (test, compare to baseline)
Iteration: 1-2 weeks (if first attempt isn't good enough)
Total: 2-4 weeks typical

When Full Training Is Needed

Full training (not fine-tuning) when:

Building a competitive advantage product
Domain is completely different from existing models
You have massive proprietary data (millions of examples)
Data privacy requires local-only model

This is rare for typical businesses.

Training Timeline Details

Phase	Duration	Requirements
Data collection	2-4 weeks	Domain experts
Data cleaning	1-3 weeks	Data engineers
Training	1-4 weeks	GPU compute
Evaluation	1-2 weeks	Testing framework
Deployment	1 week	DevOps

Data Requirements

How much data you need:

Fine-tuning: 100-10,000 examples minimum
Full training: Millions of examples
Quality matters: Bad data = bad model

Greene Solutions Approach

We recommend the simplest approach that works:

Start with RAG—no training needed
If that fails, try prompt engineering
If that fails, consider fine-tuning
Full training only if absolutely necessary

Not sure if you need training?

We'll assess your use case and recommend the right approach.

Book Free Assessment →