When Should You Actually Train an AI Model? (Rarely — But Sometimes It Makes Sense)

In a previous article, I argued that most senior software engineers should not be trying to train AI models — especially when building systems for SMEs. That position often raises a fair follow-up question:

“So when should you train a model?”

The honest answer is: rarely — but sometimes it does make sense.

This article explores those exceptions, and, just as importantly, explains how to recognise when training a model is the wrong tool for the job.

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The Default Assumption Should Be: Don’t Train

Before we look at the exceptions, it is important to set the baseline.

Training an AI model should never be the default choice. It introduces significant overhead:

• Data collection and cleaning
• Labelling and validation
• Infrastructure costs
• Ongoing maintenance and monitoring

For most software projects — even AI-powered ones — existing models combined with prompts, guardrails and retrieval will solve the problem more effectively and with far less risk.

If an off-the-shelf LLM with domain context can do the job, training your own model is usually unnecessary engineering.

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The First Valid Reason: You Have a Truly Unique Dataset

One of the few legitimate reasons to train a model is when you possess data that:

• Is proprietary
• Is unavailable publicly
• Cannot be effectively retrieved at runtime
• Represents patterns rather than simple facts

Examples might include:

• Years of operational sensor data
• Large volumes of annotated inspections or reports
• Historical behaviour data that reveals trends, not text answers

In these cases, knowledge is embedded in patterns — not documents a model can simply read.

Even then, the bar should remain high. Having data is not enough; it needs to be useful, clean and representative.

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The Second Reason: You Need Deterministic Classification

Another scenario where model training can make sense is constrained classification problems.

Examples include:

• Sorting inbound messages into fixed categories
• Flagging documents that meet specific criteria
• Detecting anomalies within structured data

In these cases:

• The input format is consistent
• The output categories are known
• Accuracy can be measured objectively

Small, focused models — or even traditional ML techniques — can outperform large language models here.

Crucially, these are narrow problems, not general reasoning tasks.

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The Third Reason: Latency, Cost or Isolation Requirements

There are practical considerations that sometimes justify training:

• Strict latency constraints
• Offline or air-gapped environments
• Regulatory requirements around data residency

In these situations, running a small local model can be more predictable than calling external APIs.

However, this is a trade-off decision — not an argument that training models is inherently better.

It also assumes you are prepared to own and maintain that model long-term.

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What Usually Is Not a Good Reason

Some motivations sound compelling but rarely stand up to scrutiny:

• “We want control” — control without expertise becomes liability
• “Our data is special” — often it isn’t
• “This will differentiate us” — customers buy outcomes, not models
• “We don’t want vendor lock-in” — self-maintained AI is its own lock-in

These reasons often mask discomfort with dependency rather than a genuine technical need.

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RAG vs Training: A Crucial Distinction

One of the most common mistakes is choosing training when retrieval would do.

If your problem is:

• Answering questions about policies
• Explaining regulations
• Summarising internal documentation

Then retrieval-augmented generation is almost always superior.

RAG offers:

• Up-to-date information
• Auditability
• Simple updates
• Clear data ownership

Training a model to “remember” facts is expensive and brittle compared to letting it read them when needed.

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The Senior Engineer’s Role: Saying No Early

One of the most valuable contributions a senior engineer can make is preventing unnecessary complexity.

That often means asking hard questions:

• What failure looks like
• How correctness will be measured
• Who maintains this in 12 months
• What happens when requirements change

Model training dramatically increases the cost of getting these answers wrong.

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A Practical Decision Framework

Before training a model, a pragmatic checklist helps:

• Can an existing LLM solve this with context?
• Is RAG sufficient?
• Is the problem deterministic?
• Do we have enough quality data?
• Can success be measured objectively?
• Are we prepared to maintain the model?

If several of these answers are “no”, training is almost certainly the wrong choice.

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Final Thoughts

Training AI models is not inherently bad — but it is a specialised tool for specific situations.

Most senior software engineers add far more value by:

• Using models wisely
• Constraining behaviour
• Designing robust systems
• Focusing on business outcomes

Knowing when not to train a model is often the clearest sign of experience.