How to format training data for a domain-specific AI model?

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I’d like to train / fine-tune a base AI model on domain-specific knowledge. My goal is to create an AI model that can generate highly accurate questions and answers in this limited domain.

I’m beginner in ML, but I’m constantly learning about the field. Although I extensively searched for an answer, and took some of the courses here, I’m still not sure about some aspects of AI training.

I have all the necessary raw data, but it’s currently in different formats such as PDF and HTML texts. I know that I need structured training data, but I’m not sure what the best format should be. I’m planning to utilize Gemini with a Python script to convert my raw data into a suitable dataset.

Here are my main questions:

  1. What is the best format for training data in my case? Should a dataset always consist of “input-output” pairs format, which I see all the time in the examples? Intuitively, I would think that a different format such as {"term": "...", "definition": "...", "examples": "..."} could be more useful to train my model, but I got a feeling that AI is actually not learning like humans. So this might not teach the AI the knowledge that it needs to use. So, is it always better / necessary to use the input-output Q&A pairs to fine tune the AI? In my case, it’s a bit tricky to cover my materials with Q&A pairs.
  2. How should I train for both question generation and answering? Should I train two separate models: one for question generation and one for answering user queries about the domain? Can a single fine-tuned model handle both tasks?
  3. Best practices for fine-tuning an AI model on specific domain knowledge. What are common mistakes beginners make when training a domain-specific AI? Any recommended models, frameworks, or tools for training in my case? I learned that there are different ways to tune an AI such as prompt engineering, RAG, fine-tuning, and others. I think fine-tuning is necessary in my case as I require very high accuracy on the specific domain. Are there any other / better methods that I can explore?

I’d really appreciate your advice. Any insights or examples would be incredibly helpful. Thanks in advance!

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Did you check the Generative AI with Large Languages Models Specialization?

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To format training data for a domain-specific AI model, follow these steps:

  1. Collect High-Quality Data – Gather relevant, clean, and diverse data specific to your domain.
  2. Structure the Data – Organize data in a structured format (CSV, JSON, or database) with clear labels.
  3. Preprocess the Data – Normalize text, remove duplicates, handle missing values, and ensure consistency.
  4. Label the Data – Use manual labeling or automated tools to assign correct categories or annotations.
  5. Split the Dataset – Divide the data into training, validation, and testing sets for model evaluation.
  6. Optimize for Performance – Balance the dataset, remove bias, and format inputs based on the AI model’s requirements (e.g., tokenization for NLP).
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No, I thought it wasn’t directly covering finetuning. I checked other courses such as Finetuning with Lamini. If the specialization covers AI training and finetuning, I’ll definitely check it out. Thank you!

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Thank you! These are all helpful and clear to me. But I’m having issues more on the details and nuances of structuring a dataset. From the sources I looked into, it’s still not clear to me if I should always use an input-output style format in my dataset. I couldn’t figure out if I can use context knowledge structured in different categories such as “term”, “definition”, “examples”, “sources”, etc. and if these additional context info would really teach the model and help answering the domain questions more accurately.