Fine-Tuning a P&ID Model: Model vs Prompt Fine-Tuning

In the world of P&ID (Piping and Instrumentation Diagram) automation, accuracy matters.
Small differences in tag numbers, layout, or instrument annotations can have significant downstream impacts in cost estimation, procurement, and project execution.

When we set out to build a better way to automate P&ID annotation and correction, we faced a key decision:

Should we fine-tune a language model itself? Or should we fine-tune the prompts we give it?

This blog explores both approaches - their trade-offs, practical realities, and how we made local-first fine-tuning a cornerstone of our solution.

Read More: Automated Data Extraction from P&IDs Using eAI

Two Ways to Fine-Tune AI for P&IDs

When engineers think about making AI smarter for their specific needs, there are two major paths:

Path	Description
Model Fine-Tuning	Training or adjusting the model weights based on new data. The model “learns” permanently.
Prompt Fine-Tuning	Carefully structuring the examples and instructions given to the model, without changing the model itself.

Both paths can work — but they serve different needs.

Model Fine-Tuning

In model fine-tuning, we modify the model itself.
For example, we could take a general-purpose model like Phi-3-mini and teach it specific patterns seen in P&ID tag corrections.

Steps:

• Collect a training dataset (corrected tags, layouts, typical errors)
• Fine-tune the model (using Hugging Face, LoRA, or QLoRA)
• Export a new model (e.g., .gguf for use with Ollama)
• Deploy the fine-tuned model locally

{
  "messages": [
    {
      "role": "system",
      "content": "You are an annotation assistant. Correct symbols based on context and patterns observed in similar tags."
    },
    {
      "role": "user",
      "content": "Given the following symbols: 22-PI-214, 22-PG-102, A1-PG-58\nWhat should A1-TI-4B be corrected to?"
    },
    { "role": "assistant", "content": "22-TI-48" }
  ]
}

Advantages:

• The model becomes smarter in general.
• No need to show examples every time at inference.
• Potentially faster and more consistent responses.

Disadvantages:

• Requires GPU resources for training.
• Harder to update: fine-tuning again means re-training the model.
• Risk of forgetting old knowledge (“catastrophic forgetting”).
• Larger file sizes.

Prompt Fine-Tuning

Instead of changing the model, we change the instructions and examples we feed into the model.

Prompt: Given the following symbols: 41-TT-252, 41-PI-160, L1-FT-99\nWhat should L1-FT-99 be corrected to?
Response: 41-FT-99

We developed a dynamic system where:

• Users can add new correction examples over time.
• Newer examples automatically become more important.
• Frequently matched examples rise in priority.
• Old examples fade away unless still relevant.

Advantages:

• No GPU needed — updates happen instantly.
• Easy to manage examples dynamically.
• Transparent: users can see, add, or remove examples.
• Fully local: no retraining required.
• Model remains general-purpose.

Disadvantages:

• Limited by prompt length (e.g., tokens limit in LLMs).
• Slightly slower inference for very large prompts.
• Depends on model’s ability to generalize from prompts.

Our Choice

Given the industries we work with (EPCs, manufacturing, operations), local-first was non-negotiable.

Our solution offers:

• Local deployment: Models run fully offline on users’ machines (via Ollama).
• Local prompt fine-tuning: Users can customize examples to improve accuracy for their own plant, project, or organization.
• Optional remote model fine-tuning: As users get more comfortable, they can optionally fine-tune a model remotely (e.g., Hugging Face) and pull the result back locally.

This provides maximum flexibility:

• Start with prompt fine-tuning: lightweight, easy, controllable.
• Scale to model fine-tuning: when users want deeper customization.

Why It Matters

P&IDs are critical engineering documents. They are messy, diverse, and often customized project-by-project. A rigid, black-box AI system would fail in this environment. Giving users control — whether through dynamic prompt management or offering fine-tuned models when they’re ready — builds trust, ensures adaptability, and makes AI feel like a true extension of the engineering team, not a replacement for it.

Conclusion

Fine-tuning a P&ID model isn’t just about technical accuracy — it’s about giving users the power to shape the AI to their specific context.

By combining prompt fine-tuning for immediate flexibility and model fine-tuning for deeper specialization, we create a local-first platform that evolves alongside the needs of each project, plant, or organization.

Explore eAI or Book a Demo today.