Training vs. Tuning - Choosing the Right AI Strategy
By Anand George
Introduction: Not All AI Is Built the Same
When adopting AI for engineering workflows — whether for symbol detection, tag correction, or template recognition — teams often ask:
“Should we train a model from scratch or fine-tune an existing one?”
The answer isn’t just technical — it depends on your data, your constraints, and how fast you need results. In this post, we break down training vs. tuning in the context of engineering documents like P&IDs, and help you pick the right path for your use case.
What Does “Training” Really Mean?
Training from scratch means:
- Defining your model architecture (e.g., CNN, transformer, YOLO)
- Starting with random weights
- Feeding in labeled examples
- Letting the model learn patterns over many epochs
Pros:
- Full control over architecture
- Tailored to very specific data formats
- Potential for better performance — if done right
Cons:
- Requires a large labeled dataset
- Time-consuming
- Computationally expensive
- Difficult to debug
Best for: Organizations with unique data, rare symbology, or long-term internal AI investment
What About “Tuning”?
Fine-tuning or transfer learning involves:
- Starting with a pre-trained model (e.g., trained on general images)
- Replacing the final layers
- Training on your custom dataset to adapt the model to your domain
Pros:
- Works well with small datasets (even a few hundred samples)
- Much faster to train — often within hours
- Requires less compute and storage
- Still achieves excellent results
Cons:
- Limited flexibility (you inherit architecture and bias of base model)
- Risk of overfitting if not carefully regularized
Best for: Teams with some labeled data who want fast, effective results without reinventing the wheel
Real-World Example: Inline Instrument Detection
Let’s say you want to detect inline instruments in P&IDs:
- Training from scratch: You’ll need 2,000–5,000 labeled samples, a full training pipeline, and lots of tuning
- Fine-tuning YOLOv5 or MobileNet: You can start with ~300–500 labeled images, fine-tune in under 2 hours, and get >90% accuracy
At Storm Consulting, we almost always start with tuning — then scale up if the data or use case demands it.
Bonus: When to Use Prompt Engineering Instead
Some problems don’t need a trained model at all. If your use case involves:
- Tag correction
- Classification of known formats
- Contextual disambiguation
… then a well-designed prompt for an LLM like OpenAI may do the job. No training, no tuning — just smart prompt design.
This is especially useful for handling misread OCR tags or inferring missing data.
Summary Table: Train vs. Tune vs. Prompt
| Approach | Data Required | Time to Deploy | Flexibility | Use Cases |
|---|---|---|---|---|
| Train | High (>2k samples) | Weeks | Very High | Rare symbols, proprietary formats |
| Tune | Medium (~300–500) | Days–Hours | Moderate | Common symbols, layout reuse |
| Prompt | None (just logic) | Immediate | Low | Text correction, inference |
Conclusion: Strategy Follows Context
There’s no one-size-fits-all strategy for engineering AI. Instead, think in terms of:
- How unique is your data?
- How fast do you need results?
- How much data can you label?
At Storm Consulting, we help teams find the fastest path to accurate results — whether that means full training, lightweight tuning, or no model at all.