As organizations of all sizes integrate generative AI into their workflows, business leaders need to understand what happens before and after the prompt.
For most generative AI users, the seeming simplicity of entering a prompt that produces results to assist with business tasks masks a more complex reality that can't be ignored. Training data, large language model (LLM) configuration, and transparency aren’t just technical details, they’re business-critical concerns. Leaders who overlook these details can suffer financial setbacks, reputational damage, and even legal exposure.
To manage these risks, here are the questions you should ask before integrating generative AI into your operations.
Is The LLM Right-Sized for Your Business Needs?
LLMs require compute power, training cycles, and feedback loops, all of which come with costs. A general-purpose model might sound appealing, but if it’s overbuilt for your use cases, you’ll pay more and wait longer for results that could have been delivered faster by a leaner system.
For instance, Zoho Zia LLM comprises three models with 1.3 billion, 2.6 billion, and 7 billion parameters, enabling model optimization for the appropriate user context. By striking the proper balance between power and resource management and aligning the complexity of the prompt with the properly sized model, LLMs can be right-sized for business needs.
Smart vendors provide LLM options tailored to specific tasks and allow you to swap them out as your needs change. Avoid vendors that lock you into inflexible contracts. Flexibility isn’t just a feature; it’s a sign that the vendor is prioritizing your ROI over their own margins.
How Was The LLM Trained?
AI firm Anthriopic recently agreed to pay $1.5 billion to settle a class action lawsuit filed by authors who said the company stole their work to train its AI models.
The settlement comes as other big tech companies, including Meta face lawsuits over similar alleged copyright violations.
The Anthropic deal to resolve the plaintiffs' remaining legacy claims, and the other lawsuits, highlight how some notable companies approach training LLMs on external, proprietary content. As the courts determine whether the outputs from these models are transformative enough to qualify as "fair use," the Anthropic deal requires the approval of the U.S. District Court, and debate continues.
But legal precedent does not guarantee ethical clarity. The surge of new LLMs entering the market has made training methodology a significant differentiator. When training data includes toxic or extremist content, as seen in the recent Grok chatbot controversy on X, the outcomes can be harmful. Grok’s antisemitic rant, triggered by a fake account, clearly reflects its training environment.
To prevent risky outputs, business customers should inquire about the training sources vendors use. Ideally, models are trained on synthetic data and anonymized customer experience statistics instead of vendors scraping internet content from questionable sources.
Most reputable LLM vendors also publish documentation or whitepapers that explain their model architecture, training data sources, and design principles.
Transparency isn’t optional. If a vendor refuses to disclose its training practices during the sales process, don’t expect clarity if things go wrong.
The most forward-thinking vendors aren’t scraping the internet or using their customers' information for training data; instead, they’re developing LLMs internally across products with privacy in mind.
Ask the Hard Questions
When assessing a tech vendor, don’t just ask what their model can do, ask how it was built. What data sources were used? Were they synthetic, proprietary, or scraped from the open web? How is bias reduced? What safeguards are in place to protect user privacy?
These aren’t just technical questions. They’re tests of trust.
Choosing the right LLM vendor isn’t just a procurement decision; it’s a strategic commitment to transparency, accountability, and long-term value. As generative AI becomes integrated into everything from customer service to product development, business leaders must view vendor selection as a due diligence process, not a leap of faith.
Vendors who prioritize privacy, size their models appropriately, and provide transparency about training practices are best positioned to support your business. In a landscape where technology advances rapidly, understanding what occurs before and after the prompt is essential.