In Veritas—Why High-Quality Data is the Foundation of Trustworthy AI

When it comes to building large language models (LLMs), the adage “garbage in, garbage out” rings truer than ever. Yet many organizations overlook a critical truth: the quality of training data determines not just accuracy but also trustworthiness and safety. The Latin phrase in veritas, meaning “in truth,” reminds us that foundational integrity lies in the sources we choose—especially when balancing freely available web-scraped data with curated, static datasets.

Why Web-Scraped Data is a Double-Edged Sword

Web scraping offers vast, dynamic datasets at low cost. However, this approach carries hidden risks:

1. Privacy and Security Vulnerabilities Unstructured web data often includes personal information scraped without consent, exposing models to prompt injection attacks (where adversaries exploit models to leak sensitive training data). Such flaws can compromise user privacy or enable harmful outputs 1.
2. Unreliable Accuracy Over Time Web content evolves—old pages vanish, misinformation spreads, and biased sources persist unchecked. Models trained on this “living” data may perpetuate outdated views or amplify societal biases 0.
3. Legal Landmines Scraped data frequently violates terms of service or copyright laws, leaving organizations legally exposed. Purpose limitation principles—ensuring data is collected for specific uses—are hard to enforce when training models on broad web content 1.
4. Risk of Data Poisoning Adversaries can deliberately inject malicious “poisoned pill” data into uncurated datasets, subverting model behavior to generate biased outputs, create hidden backdoors, or trigger harmful actions in response to specific prompts 1. For instance, poisoned data might embed code that instructs models to leak user information, spread propaganda, or produce toxic content systematically. The chaotic nature of web-scraped sources makes detecting and neutralizing such threats computationally and ethically overwhelming for developers.

The Value of Static, Curated Datasets: In Veritas

Published and “frozen in time” datasets—like academic journals, authoritative books, or vetted corpora—offer a stark contrast. Their benefits include:

1. Consistency and Accountability By using static data, developers anchor models to verified facts rather than fleeting web trends. This reduces errors introduced by inconsistent or biased sources 0.
2. Reduced Ethical Risks Curated datasets are more likely to align with ethical standards (e.g., GDPR compliance) and minimize exposure of personal information 1.
3. Long-Term Reliability “Frozen” data provides a stable foundation for model training, ensuring reproducibility and reducing the likelihood of unintended outputs caused by dynamic, untrusted sources.

Building Trust Through Data Integrity

The phrase in veritas embodies this principle: truthfulness starts with transparency in sourcing. Organizations must prioritize datasets that are ethically collected, rigorously vetted, and grounded in accountability.

For LLM developers, the choice is clear: Web scraping may offer shortcuts, but it risks tainted outputs and ethical backlash. By leveraging static, authoritative data, we build models that reflect veritas—truthful, reliable tools for business, research, and innovation.

Final Thought: The future of AI isn’t just about complexity—it’s about integrity. Let’s choose our data with care so the truth in our models matches the trust we ask from users.

Citations: 0 Context on experiments introducing errors into training data and Telmai’s role in addressing quality issues. 1 Privacy risks, prompt injection attacks, legal challenges—and now poisoning—tied to web-scraped data.