Artificial intelligence is increasingly permeating the digital environment — from recommendations on streaming services to the generation of complex texts, program code, and user behavior analysis. At the heart of this technological leap are large language models (LLMs) such as ChatGPT, Claude, Gemini, and others. They work on the basis of data arrays collected, processed, and analyzed using modern AI tools.
This article will explain how data collection for LLM works, why they need huge amounts of text information, and what role mobile proxies, parsing methods, and other technical solutions play in this process. You will also learn what sources are used, how data processing is made ethical and secure, and why artificial intelligence for data collection is a fundamental part of the entire LLM ecosystem.
What are LLMs and why do they need data?
Large language models (LLMs) are the foundation of modern artificial intelligence systems capable of generating text, answering questions, analyzing information, and even writing code. Their capabilities directly depend on the diversity, completeness, and quality of the data they were trained on.
Let’s start by taking a closer look at the concept of LLM and their role in the data collection process.
How large language models work
LLMs (Large Language Models) are algorithms trained on huge text corpora. They use transformer architecture and work by predicting the next word in a sentence based on context. The greater the volume and diversity of data, the more accurately the model understands language, intonation, styles, and even semantic nuances.
Collecting data with LLM does not mean that the models themselves “walk” the internet. Instead, developers collect news sites and books, forums, and technical documentation in advance. This data is cleaned, structured, and presented as training material.
The role of data as training material
Data is the fuel for AI. Without it, even the most powerful model cannot learn to work. The amount of data collected can reach hundreds of billions of words, and sometimes trillions of tokens. Quality is also important here: a balanced representation of different languages, topics, and styles.
Automated solutions are actively used to collect information:
- artificial intelligence for data collection;
- crawlers;
- mobile proxies.
The latter are especially important when it comes to bypassing geographical restrictions and maintaining anonymity when scanning websites.
What data is collected for AI training?
LLM training requires diverse, representative, and large-scale data sets. Developers do not limit themselves to a single category. They try to cover as many formats and sources as possible so that the trained model can adapt to different use cases.
Text, code, images, and other formats
Text data forms the basis of any training corpus:
- articles;
- blogs;
- forums;
- books;
- documentation;
- correspondence and news feeds.
However, machine learning for data collection increasingly includes other formats. For example, code (Python, JavaScript, HTML) for training models such as GitHub Copilot. Or images with captions for multi-format models.
LLM training for data collection is becoming comprehensive: the model learns not only from plain text, but also from context — visual, logical, and structural.
Open and closed sources
Most of the data comes from open sources: Wikipedia, GitHub, StackOverflow, news portals, academic publications. This is the legal and ethical basis for training, as open data is generally available for analysis and use.
However, with the development of AI, the issue of closed or semi-open data is increasingly being raised — for example, from social networks, marketing platforms, or forums with restricted access. Their use requires careful compliance with rules, including circumventing restrictions using proxies and anti-detection tools.
Data ethics and confidentiality
In the era of GDPR, DSA, and other regulations, ethics has become an integral part of any AI training process. Processing personal data without user consent can lead to legal consequences and reputational damage for the developer.
Therefore, large teams implement procedures for filtering sensitive information, use secure environments for collection, and employ mobile proxies to minimize the risk of identifying the user or data source.
Data collection tools and methods for LLM
The development and training of large language models is impossible without a well-established data collection system. To ensure the quality, scale, and diversity of training material, teams use a combination of technologies. It is important to strike a balance between automation, ethics, and technical efficiency.
Web scraping with proxies
Web page parsing is one of the most common ways to extract content. It can be used to collect text, comments, prices, news, codes, and other useful information. However, websites are increasingly protecting themselves from automatic data collection by implementing captchas, anti-bot protection, and IP filtering systems.
In such cases, proxy servers and anti-detection browsers for automating data collection are used. Mobile proxies and IP rotation allow you to bypass restrictions by imitating the behavior of a regular user. This is especially important when mass scanning resources, where you need to avoid being banned.
Using APIs and synthetic data
An alternative and “cleaner” approach is to collect information through official APIs. Many platforms (YouTube, Reddit, Twitter/X, Wikipedia) provide programmatic access to their data, allowing you to obtain structured and reliable information without the risk of being blocked.
In addition, synthetic data created manually or by other AI models is used in LLM training. This is useful for training in situations where there is a shortage of “live” examples, such as in highly specialized topics or when training generative models and dialogue systems.
Data preprocessing and annotation
Data collection is just the beginning. It is important to clean it up from noise, duplicates, spam, and irrelevant content. Annotation is also necessary — marking up semantic units, tagging, categorization. This allows AI to not just “read,” but to learn meaningfully from examples: to understand what a question is, where a dialogue begins, and how tables and codes are structured. The result is a high-quality, structured, and diverse training corpus that can give LLM a wide range of knowledge and skills.
How AI uses collected data in real-world tasks
Collected and prepared data becomes the foundation on which dozens of applied solutions are built. LLM and other AI systems are capable of not just “memorizing,” but also extracting patterns, drawing conclusions, and predicting behavior.
Content generation and automation
One of the most popular areas of application is automatic content creation. Companies use LLM to generate product descriptions, social media posts, chatbot responses, and even code. This allows them to dramatically reduce the time spent on routine tasks and scale their processes.
This automation is possible thanks to training LLM on large amounts of diverse data, including texts, templates, stylistic constructions, and examples of live communication.
Data analysis and predictive models
AI is actively used for analytics: it can identify hidden patterns, segment audiences, and find deviations in user behavior. Machine learning is used to create predictive models that can forecast demand, churn, product interest, or even the likelihood of a system breach. All of this is the result of working with high-quality, curated data sets.
Training recommendation logic systems
When you see a selection of products “you may like” on a marketplace, there is a trained model behind it. It analyzes the behavior of millions of users, remembers preferences, finds similarities between products, and provides relevant suggestions.
Data on interactions in clicks, purchases, and viewed products is particularly important for such models. The more data there is, the smarter the recommendation system works.
Automating data collection with LLM
LLMs can not only learn from data, but also help collect it. They become part of parsing, filtering, and analysis tools, replacing traditional scripts and manual work.
Using LLM for parsing and analysis
LLM scenarios are already being used to solve the following tasks:
- classifying and filtering content when collecting from websites;
- extracting structured information from unstructured text;
- generating suggestions for improving data structure;
- determining the languages, styles, and tone of the collected text.
This makes automated data collection with LLM more flexible and intelligent compared to classic parsers.
Scenarios with proxy and anti-detection browser integration
To bypass website protection and avoid being blocked, data collection tools are increasingly being supplemented with mobile proxies and anti-detection environments. This allows you to effectively collect information from different IP addresses, imitating the behavior of regular users without violating security systems.
In conjunction with LLM, such scenarios become particularly powerful: the model processes incoming data on the fly, filters out junk, adapts to changes on the website, and selects the necessary fragments for analysis.
Prospects and risks of using data
When it comes to collecting and using large amounts of information, especially in the context of AI and LLM, it is impossible to ignore the opportunities and threats. Technology is evolving rapidly, and with it, the list of ethical, legal, and technical challenges is growing.
Risks of data leakage and reuse
One of the main issues is confidentiality. Even if data is collected from public sources, the question of its reuse remains acute: many LLMs are trained on content whose authors are unaware of this.
There is also a risk of:
- leaks of personal information;
- generation of responses based on sensitive or protected data;
- copyright infringement when regenerating original texts.
All of these scenarios require strict source control, regular audits, and the implementation of ethical standards in the training and use of models.
Prospects for generative data collection
On the other hand, new approaches are emerging, such as generative models and data collection, where AI does not simply learn from ready-made material, but helps to generate additional training content itself. This can include:
- creating synthetic texts for training;
- generating variations of given templates;
- simulating dialogues and user behavior.
This approach solves the problem of a lack of high-quality data, especially in highly specialized fields, and speeds up the process of scaling AI systems.
