How Delivery/Project Leaders Learned : Training & Tuning

Somewhere along the way, “training” and “tuning” became the new “blockchain” and “microservices.” - widely used, poorly understood, and dangerously overconfident.

Suddenly, every second delivery leader is saying things like: “We should train our own model.” “Let’s tune it for better accuracy.” "We trained the model for automation" ..etc

And I sit there thinking… Train what? Tune what? You have not even tuned your sprint backlog properly.

The Problem Is not the Words, It’s the Blind Confidence. Look, I am not against people learning new things. In fact, I encourage it.

But there is a difference between:

• Understanding something vs
• Dropping buzzwords in review meetings, exec presentations or client calls like seasoning on biryani,  except here, it is all masala and no rice.

Right now, we have a wave of few “AI-aware” leaders who believe:

• Uploading data = Training a model
• Changing a prompt = Tuning a model

If that were true, we would all be AI researchers by now.

So Let’s Fix This in Plain English: Forget jargon. Let me explain this, the way it actually works.

What is Training a Model?

Training is where the real heavy lifting happens. Think of it like teaching a child from scratch:

• You show examples
• You correct mistakes
• You repeat… a lot

A model during training:

• Looks at a huge amount of data
• Learns pattern from that data
• Adjusts its internal parameters (millions or billions of them)

It is not just “feeding data.” It is mathematical optimization at scale.

Simple analogy: Training a model is like: Teaching someone an entire language from zero,  grammar, vocabulary, context, everything.

In reality, training involves:

• Massive datasets
• GPUs burning money like Diwali crackers
• Algorithms adjusting weights through backpropagation

And yes… it is expensive, slow, and complex.

So next time someone says:

“Let’s just train our own model.” "We trained the model to..."

You might want to ask:

“With what data, what infrastructure, and whose budget?”

 

What is Tuning a Model?

Now comes tuning, the part most people think they are doing. Tuning is not building from scratch. It is refining something that already exists. There are different levels of tuning:

1. Fine-Tuning

You take a pre-trained model and:

• Train it further on specific data
• Make it better at a particular task

Example: A general AI model → fine-tuned for legal contracts

2. Prompt Tuning (what most people actually do)

This is:

• Changing how you ask questions
• Structuring inputs better

Let’s be honest, this is what 80% of teams call “AI tuning.” And there is nothing wrong with it. Just don’t call it “model tuning” in a strategy meeting.

3. Parameter Tuning

Adjusting things like:

• Learning rate
• Batch size
• Model behavior settings

This is closer to real ML work.

 

Here is what actually happening in most delivery teams:

And again, nothing is wrong with using APIs. Just don’t over sell it like you have reinvented AI. This confusion is not just funny,  it is dangerous.

Because:

• Clients / Business get unrealistic expectations
• Teams get vague directions
• Budgets get allocated blindly

And eventually… Someone has to explain why “training the model” did not magically solve the problem.

 

When Should You Actually Train a Model?

Almost never (for most enterprises). You should consider training only if:

• You have massive proprietary data
• Off-the-shelf models don’t work
• You can afford infrastructure and expertise

Otherwise? Use existing models. Adapt them. Build solutions around them.

When Should You Tune?

All the time,  but intelligently.

• Use prompt engineering for quick wins
• Fine-tune when domain specificity matters
• Optimize based on real feedback, not assumptions

The Real Skill Is not Training, It’s Thinking

Here’s the uncomfortable truth: AI success is not about:

• Training models
• Tuning parameters

It is about:

• Defining the right problem
• Using the right approach
• Knowing what not to build

In conclusion, the next time someone confidently says: “Let’s train and tune our own model or We have trained the model to... ”

Pause. Smile. And gently ask: “Are we building intelligence… or just building slides?”

Because in today’s world, AI does not fail because of technology. It fails because of vocabulary-driven Delivery Leadership.