Training Data

Training data is the set of examples a machine learning model learns from. In supervised learning, each example pairs an input with the correct output, such as a keyword and its intent label. For large language models, training data is the huge collection of text, code and other content the model absorbed during pre-training, which shapes what it “knows” without looking anything up.

How it works

A model adjusts its internal parameters until its predictions on the training data are as accurate as possible. To check that it has learned general patterns rather than memorising, part of the data is held back as a test set (MLforSEO’s practicals often use an 80/20 train/test split). The quality, size and balance of the training data usually matter more than the choice of algorithm.

For LLMs, MLforSEO’s agentic SEO course separates training data from retrieved data. Training data is internalised knowledge, fixed at the model’s knowledge cutoff; your content cannot change today’s answer through it. Retrieved data is fetched at query time. Content that is consistently cited and referenced across the web is more likely to be included in future training sets.

How it’s used in SEO and marketing

• Building custom classifiers: labelling a few hundred of your own queries or pages to train an intent, topic or synthetic-query classifier.
• Fine-tuning: adapting a pre-trained model with a smaller, domain-specific data set (see fine-tuning).
• AI crawler policy: deciding whether to allow training crawlers such as GPTBot in robots.txt, separately from search and retrieval crawlers.
• Brand presence: earning consistent, factual mentions in widely used sources that may feed future models.

Example

In MLforSEO’s lab on distinguishing synthetic from user-initiated queries, you export queries from Search Console, label a few hundred by hand as USER or SYNTHETIC, and train a classifier on them. The model’s accuracy depends heavily on how representative those labelled examples are of your industry, so the lab recommends domain-specific examples rather than generic ones.

Limitations and things to watch

• Garbage in, garbage out: errors and inconsistent labels in training data become model errors.
• Training data can encode bias, for example by over-representing one market, language or customer type.
• Testing on the same data you trained on hides overfitting.
• For LLMs, training data goes stale. Facts that changed after the cutoff need retrieval to be correct.
• Check licences and privacy rules before using customer or scraped data for training.

Learn it on MLforSEO

• Synthetic vs User Query Classification Notebook with Rule-Based and Machine Learning Detection
• How to do keyword clustering with KeyBERT
• Sentiment Analysis: A Deep Dive Into the Theory, Methods, and Applications
• The Characteristics of a Machine Learning Problem: A Task, Data and Solution Framework for SEOs
• Introduction to Machine Learning for SEO (course)
• AI Search Optimisation & Agentic SEO (course)