Why thousands of sample documents matter for categorization in large Relativity workspaces

How many sample documents does it take to teach a categorization model in a Relativity workspace? A few dozen? Hundreds? The right answer, in practice, is a couple thousand. Here’s why that modest-sounding number truly makes a difference, plus some practical tips you can use on real projects.

Let’s set the scene

When you’re sorting a mountain of documents—emails, reports, memos, PDFs, the works—the goal is simple: train a model that can predict how new documents should be categorized. That means giving the model enough examples to learn from, so it can spot patterns, terms, and contexts it hasn’t seen yet. If you’re starting with Relativity or a similar platform, you’re balancing quality with speed. You want accuracy, without spending years labeling every single file.

Why “a couple thousand” hits the sweet spot

• Diversity matters. A large workspace isn’t one uniform blob of text. It holds different departments, time periods, jargon, and client-specific phrases. A couple thousand documents let you capture this variety—so the model doesn’t get tripped up by a new vendor name, a rare abbreviation, or a regional spelling difference.

• Context is king. Categorization isn’t only about keywords. It’s about context, phrasing, and how terms relate to one another across documents. More examples help the model understand that “confidential” in one department might look different in another, or that a certain phrase signals a category only when paired with other terms.

• Outliers get accounted for. Your data isn’t pristine. It includes edge cases, mistakes, and unusual formats. A larger sample helps the model learn to handle those outliers gracefully instead of cratering performance on them.

• Generalization over memorization. The aim is to predict well on unseen data, not just on the files you’ve labeled. A couple thousand well-chosen samples give the model a shot at generalizing to new documents that still fit real-world patterns.

What happens if you go smaller?

• Fewer than a hundred or a few dozen: It’s like teaching from a sketchbook. You’ll miss important variations, and the model may be overly confident about rare cases because it hasn’t seen enough examples to question itself. Accuracy on new documents tends to wobble.

• Hundreds: Better than a tiny sample, but still risky if those hundreds don’t cover enough contexts. You might end up with a model that’s good at recognizing the most common patterns but stumbles when a file is a little unusual or from a niche domain.

• The middle ground can feel convenient, but it often comes with hidden costs. Time spent labeling might be lower, but the performance lift you get from a couple thousand samples is usually worth it in the real world.

How to size up in practice

So, how do you land on that couple-thousand target without getting bogged down? Consider these checkpoints:

• Start with a representation of the workspace. Think about the different teams, timeframes, and document types. Do you have emails, contracts, internal memos, and external correspondence all in there? If you can map the major categories or contexts, you’re halfway to a solid sample plan.

• Strike a balance between depth and breadth. You don’t want only the longest documents or the densest ones. Include short messages, long reports, and everything in between. Diversity in length, format, and topic matters as much as diversity in content.

• Use stratified sampling when possible. If you know there are distinct subgroups (e.g., litigation vs. regulatory filings, or different client accounts), pull samples from each subgroup. That helps the model understand variations across the workspace rather than clustering all learning in one corner.

• Include a mix of typical and atypical examples. Don’t shy away from tricky cases. A few challenging documents train the model to handle ambiguity better.

• Reserve a validation set. Set aside a separate chunk of documents to test how well the model generalizes. If performance on the validation set lags, you know it’s time to add more representative samples or rethink the labeling scheme.

• Label with care. Consistency in labeling matters as much as quantity. A well-documented labeling guide and a quick inter-annotator check can prevent drift and keep your sample effective over time.

• Be iterative. You don’t have to label all thousand at once. Start with a solid core, measure, and then incrementally label more to address specific gaps you uncover during validation.

Relativity-specific flavor: making the most of the platform

Relativity users aren’t just flipping switches; they’re managing complex workflows with multiple stakeholders. Here are some practical ways to apply the couple-thousand idea in a Relativity setting:

• Leverage tags and taxonomy with intention. As you label, map documents to a taxonomy that reflects how your organization actually talks about topics. A well-structured taxonomy makes it easier for the model to generalize across related categories.

• Use active learning where it makes sense. If your platform supports it, present the model with the most uncertain documents for labeling. This tends to yield big performance gains with fewer labels because you’re targeting the spots where the model struggles most.

• Track learning curves. Monitor how precision, recall, and F1 evolve as you add more samples. If gains start to plateau, you’ve probably picked up most of the useful signal from your current pool.

• Plan for drift. Workspaces evolve: new projects, new clients, new regulatory cues. Build in periodic re-training with fresh samples so the model stays aligned with how the data looks today.

• Collaborate across teams. Different groups may encounter unique document types. A quick cross-team labeling session can surface blind spots and ensure your sample covers the real-life mix.

Common pitfalls to sidestep

• Biased sampling. If labeling focuses only on the most obvious cases, you’ll end up with a model that’s excellent on easy stuff but flaky on the rest. Purposeful sampling helps here.

• Redundancy without value. Re-labeling dozens of almost-identical documents eats time without boosting learning. Aim for variety, not volume for volume’s sake.

• Skewed class distribution. If one category dominates the sample, the model may be biased toward that category. Intentionally balance the representation.

• Overfitting risk. Too much focus on the training set at the expense of a robust validation set can give you a false sense of security. Validation is your reality check.

• Inconsistent labeling. If different people label the same concept differently, the model learns conflicting signals. Clear guidelines and quick checks help.

A few real-world analogies to keep the idea tangible

• Training a chef. You wouldn’t teach a chef with only a handful of recipes. You’d give them soups, salads, mains, desserts—different flavors, textures, and techniques. Over time, the chef can improvise with confidence. A couple thousand labeled documents work the same way for a categorization model: they teach nuance, not just rote patterns.

• Sorting mail in a big office. Early on, you separate obvious categories. But you soon realize you need a broader mix of letters—some come from the unfamiliar courier, some use unusual salutations. The more representative your sample, the better you’ll sort future mail at speed.

• Building a library index. A librarian won’t index a library with only a handful of books. You need samples that cover genres, time periods, authors, and formats. Likewise, a large workspace needs a broad, representative training set to index documents accurately.

Key takeaways to carry forward

• The ideal sample size for categorization in a large workspace is about a couple thousand documents. This size balances diversity, context, and practicality.

• More than a tiny set helps the model generalize to unseen documents, reducing misclassifications and surprises on real data.

• The quality of labeling matters as much as the quantity of samples. Clear guidelines, a thoughtful taxonomy, and consistency are essential.

• Plan for ongoing learning. Workspaces evolve; so should your model. Periodic retraining with fresh samples keeps performance sharp.

• Practical Relativity workflows benefit from a mix of strategic sampling, validation discipline, and cross-team collaboration.

If you’re managing a project that involves categorization, take a moment to map out your workspace’s main document types and contexts. Sketch a quick sampling plan, aiming for that couple-thousand target, and set up a simple validation loop. You’ll likely find your models not only learn faster but also stay reliable as new data rolls in.

A final thought: data work is a bit like building a bridge. You pour the foundation with a solid, representative sample; you test it with a diverse load; and you keep reinforcing it as traffic grows. The more thoughtful your sample—and the more disciplined your labeling—the sturdier your bridge to accurate categorization becomes. If you’ve got a big workspace on the horizon, that couple thousand you’re aiming for could be the difference between a bafflingly clever model and a truly dependable one. And that’s a payoff you can feel in every project you touch.