Training data is the collection of examples that an AI system learns from to perform its tasks. Think of it like the textbooks, practice problems, and case studies you'd use to learn a new skill. Just as you'd study past financial statements to learn accounting patterns, an AI system studies training data to recognize patterns and make decisions.
For a business using AI, training data is what determines how well the AI performs. If you're building an AI to process invoices, your training data might include thousands of past invoices with annotations showing where the vendor name, total amount, and line items appear. The AI studies these examples to learn how to extract the same information from new invoices it hasn't seen before.
The quality and quantity of training data directly impacts AI performance. An AI trained on 10,000 diverse invoices from various vendors will perform much better than one trained on just 100 examples from a single vendor. The data needs to represent the real-world variety the AI will encounter, including edge cases like handwritten notes on invoices, partially obscured text, or unusual formats.
Training data can come from your own historical records, publicly available datasets, or synthetic data created specifically for training. Many businesses worry they don't have enough data to start with AI, but modern approaches like fine-tuning pre-trained models mean you can often start with less data than you think. The key is having representative examples of the work you want the AI to do.
How much training data do I actually need to build an AI system?
The amount varies significantly based on what you're building and the approach you're using. If you're training a specialized model from scratch, you might need tens of thousands of examples.
However, most business AI applications today use pre-trained models that already understand language and common patterns. For these, you might only need hundreds or a few thousand examples to fine-tune the model for your specific use case.
For example, if you're building an AI to categorize expense reports, you might start with just 500 labeled examples covering your common expense categories. The AI leverages its existing knowledge of language and business concepts, then learns your specific categorization rules from those examples.
Can I use my company's existing data as training data, or do I need something special?
You can absolutely use your existing business data, and this is often preferable because it reflects your actual operations. For an AI processing purchase orders, your historical PO database is perfect training data. However, you often need to prepare it first.
Raw data needs to be labeled or annotated to show the AI what to look for.
For example, if you have 10,000 past contracts, someone needs to mark where the key dates, payment terms, and parties appear in at least a subset of those contracts. Some data preparation can be automated, but typically you'll need human effort to create high-quality labels.
The good news is that once you've labeled a core set, you can often use the AI itself to help label additional data.
What happens if my training data has errors or biases?
The AI will learn those errors and biases, which is one of the biggest risks in AI deployment. If your training data for invoice processing primarily includes invoices from US vendors, the AI might struggle with European VAT invoices.
If your training data for expense categorization includes miscategorized expenses, the AI will replicate those mistakes. This is why data quality matters as much as quantity.
Before training, it's crucial to audit your data for systematic errors, missing categories, or underrepresented scenarios. For business applications, common biases include overrepresenting high-volume vendors while underrepresenting smaller ones, or having data skewed toward certain time periods that don't reflect seasonal variations.
Do I need to keep updating my training data, or is it a one-time thing?
It depends on how much your business processes change. If you're in a stable industry with consistent document formats and workflows, your initial training data might remain effective for years.
However, if you're in a fast-changing environment like e-commerce where vendor formats, product categories, or compliance rules evolve regularly, you'll need to update your training data periodically.
For example, if you train an AI on 2024 invoices but your vendors start using new formats in 2025, performance will gradually degrade.
Many businesses set up monitoring to track AI performance over time, then refresh training data when accuracy drops below acceptable thresholds. Some AI systems support continuous learning, where they can incorporate new validated examples without full retraining.
What's the difference between training data and a knowledge base?
Training data teaches an AI the patterns and skills it needs, like teaching someone to recognize invoice formats by showing them thousands of examples. A knowledge base provides factual information the AI can reference, like giving someone a vendor directory or a policy handbook to look up specific details.
For business AI, training data might include past expense reports so the AI learns what "meals and entertainment" versus "office supplies" look like. The knowledge base would contain your actual expense policy stating that meals over $75 require receipts.
Modern AI systems often use both. The AI uses training data to develop skills like reading documents and categorizing information, then references the knowledge base for your specific rules and data.
Can training data include sensitive business information?
Yes, and this is a major concern for businesses. If your training data includes confidential contracts, customer information, or proprietary pricing, you need to ensure it's handled securely.
The risk varies by approach. If you're fine-tuning a model on your own servers or within a private cloud environment, your data stays within your control.
If you're using a third-party AI service, understand their data policies. Some services use your data to improve their general models (meaning your data could influence outputs for other customers), while others offer private training where your data never leaves your environment or improves shared models.
For highly sensitive data, many businesses use techniques like data masking (replacing real names, amounts, or identifiers with synthetic ones) or synthetic data generation to create training examples that mimic real patterns without exposing actual information.
Zamp solves for this by keeping your business data within your secure environment. When Zamp's digital employees learn from your processes, the data stays in your systems. Zamp integrates with your existing ERPs, databases, and tools without requiring you to export or share sensitive information externally.
The Knowledge Base, where you define agent instructions, is private to your organization, and activity logs that track agent decisions remain in your control for audit and compliance purposes.
What if I don't have enough historical data to train an AI?
This is less of a blocker than it used to be.
First, modern pre-trained AI models already understand language, common document structures, and business concepts from training on massive public datasets. You're building on that foundation, not starting from zero.
Second, you can often start with a small amount of data and expand over time. For example, you might begin with 200 labeled invoices to train an initial model, deploy it with human review, then use the reviewed results to continuously improve the model.
Third, synthetic data generation can supplement real data. If you have 100 real examples of purchase orders, AI tools can generate variations to expand your training set.
Fourth, transfer learning lets you borrow from similar domains. If you don't have enough of your own customer support tickets, you might start with a model trained on public support data, then fine-tune it with your limited examples.