A larger document pool strengthens categorization by exposing models to diverse examples.

Outline at a glance

• Hook: In Relativity workspaces, categorization isn’t just about labels; it’s about teaching a system to see real-world variety.

• The core lesson: Yes, you want a large, diverse collection of documents so the model learns from many angles.

• Why diversity matters: Patterns hide in differences; a bigger, richer dataset makes classifications sturdier and less biased.

• How to put this into practice: Practical tips for building a representative dataset without chaos.

• Watchouts: Common traps when the dataset is small or skewed.

• Real-world feel: A few relatable analogies from everyday life that connect to the work.

• Quick takeaways and a friendly nudge to apply these ideas.

Is a lot of documents really needed for categorization in Relativity?

Let me frame it plainly: when you’re teaching a classifier to sort documents, having a large number of items in your workspace matters. The goal isn’t sheer volume for volume’s sake; it’s having enough variety so the model can recognize the many flavors of content it will encounter later. So, the answer is yes — to ensure diverse examples. The more angles you cover, the more confident the results become.

Why size and diversity aren’t interchangeable

Think about learning a new sport. If you practice with one type of ball, one field, and a tiny crowd, your technique may look good in that narrow setting but stumble in the real world. The same idea applies to document categorization. If your workspace only mirrors a narrow slice of what you’ll face, you train the model to perform well only on that slice. When a new document arrives with a slightly different wording, a different topic, or a mixed tone, the model might misclassify because it didn’t see anything like it during training.

Diversity is the secret sauce. It helps the system learn to:

• Recognize subtle differences: two documents may touch on the same topic but use different terms, formats, or languages.

• Handle edge cases: rare categories or unusual combinations that pop up only a few times in a dataset.

• Generalize: perform well on unseen material, not just the stuff you’ve already labeled.

• Reduce bias: a wider spectrum of content means the model isn’t leaning on a single pattern to make decisions.

A tangible way to think about it: imagine you’re teaching a child to sort a box of mixed photos. If the box only contains black-and-white portraits, the child will miss color groups, textures, and scenes that show up in travel shots or wildlife pictures. The more faces, places, and moods you include, the better the child learns to categorize accurately across the board.

What “large in the workspace” looks like in practice

You don’t need an enormous lab of documents to start, but you do want enough material to capture real-world variety. Here are practical touchpoints:

• Source variety: pull from different matter types, departments, dates, and authors. Mix emails, reports, memos, and attachments when possible. If your Relativity setup aggregates from multiple sources, make sure each source contributes a slice of the overall mix.

• Topic breadth: ensure each broad category is represented by multiple subtopics. A category like “project updates” might include status notes, risk logs, change orders, and executive summaries.

• Style and tone: include documents with formal language, casual notes, and mid-range corporate speak. Language nuance matters for models that rely on keywords and contextual cues.

• Structure diversity: not everything is a clean, single-page memo. Some items arrive as long threads, others as brief snippets or scanned PDFs. A healthy dataset includes different formats so the classifier learns to read the pattern, not the format.

• Edge cases and corner cases: deliberately include a few items that challenge the taxonomy. These are the kind of examples that help a model resist overfitting and stay robust when real-life data throws a curveball.

How to build that diversity without chaos

Here’s a friendly, actionable approach you can use without getting overwhelmed:

• Start with a baseline taxonomy: define a clear, pragmatic set of categories that makes sense for your workspace. Keep it aligned with real tasks you’ll perform, not a theoretical ideal.

• Curate in waves: rather than loading everything at once, add new material in small, purposeful batches. After each wave, review how well the categories held up and adjust as needed.

• Prioritize representativeness: for each category, aim to include items that cover different dates, authors, formats, and tones. If one category starts skewing toward a narrow pattern, diversify the samples that feed it.

• Label thoughtfully: ensure consistency in labeling guidelines. A shared glossary or quick reference sheet helps keep everyone aligned, especially when a team grows.

• Track diversity metrics: yes, you can measure it. Keep an eye on distribution across categories, formats, and sources. If a category becomes too dominant, pull in more items from underrepresented areas.

• Use sampling strategies: stratified sampling is your friend. It helps ensure that subtypes within a category get fair representation without forcing you to hand-pick every document.

Common traps and how to sidestep them

Even the best-laid plan can trip you up. Here are a few hazards and gentle remedies:

• Too small a dataset: it’s tempting to test a model quickly, but small data often hides blind spots. Aim for enough material so you can spot patterns across variations.

• Source bias: if all documents come from one department or one project, you’ll miss other ways people write or talk about similar topics. Blend sources whenever possible.

• Over-labeling edges: labeling every edge case as a separate category can clutter the taxonomy. Instead, widen a category slightly to cover similar items, then tighten as you learn.

• Ignoring dead formats: scanned PDFs, image-heavy documents, or legacy file types can behave differently. Include a representative mix of formats to teach the model to handle them gracefully.

• Rushing categorization rules: if guidelines are vague, you’ll get inconsistent labels. Create a concise, practical rubric that teams can apply consistently.

A few relatable analogies to keep the idea grounded

• Think of categorization like sorting a playlist. If your music library only has a few songs from one era, you’ll miss the breadth of genres and moods. The more songs you have from different decades, artists, and styles, the better your playlist matches varied listening moments.

• Or picture a librarian curating a reference shelf. If the shelf is skimpy and narrow, researchers might struggle to find the right tag for a nuanced topic. A generous, well-curated shelf makes discovery smoother and faster.

• Or imagine a photo organizer learning to tag images by scene. A tiny collection with repetitive subjects won’t teach it to recognize a sunset or a street scene. A broad library with distant landscapes, portraits, and candid shots helps the system group photos correctly.

What this means for Relativity users and teams

For teams using Relativity to manage information workflows, the core takeaway is simple: more diverse material in your workspace leads to smarter, more reliable categorization. It’s not about stuffing the database full for its own sake; it’s about building a training ground that mirrors the real world your work will encounter. When new documents arrive later, the model will be more adept at recognizing what’s important, what’s out of scope, and where a borderline item best fits.

A quick mental model you can carry around

• Think of the workspace as a growable library. Start with a sturdy core of well-labeled documents and steadily expand with varied samples. The classifier learns as the library grows, not just from the loudest items but from the quiet, diverse voices as well.

• If you ever wonder whether to add more items, ask: does this addition broaden the range of phrases, formats, or contexts that the system will see in day-to-day work? If yes, you’re likely moving in the right direction.

Practical tips you can try in the next sprint

• Audit your current categories: are there gaps in representation? If so, source a few items from overlooked strings of content.

• Create a mini-milotone: set a modest target to add a fixed number of diverse items each week. Consistency beats grand, one-off boosts.

• Collaborate across teams: involve folks from different groups to review labeling guidelines and bring in new examples from their daily work. Fresh eyes catch subtleties you might miss.

• Use lightweight checks: after labeling, do a quick pass to see if any category feels overstuffed or underrepresented. Rebalance as needed.

• Document decisions: a short note on why a document was placed in a given category helps future readers understand the logic and keeps the dataset coherent.

The bottom line

Diversifying the document pool in your Relativity workspace isn’t a flourish; it’s a necessity. A rich, varied collection feeds more robust learning, reduces bias, and strengthens the model’s ability to generalize to new material. You don’t need to transform your entire archive overnight, but you do benefit from thoughtful expansion — steadily, deliberately, and with an eye on real-world use.

If you’re steering categorization efforts, keep this principle close: variety yields resilience. A larger, well-rounded dataset acts like a sturdy compass, guiding your classifications through the unpredictable terrain of real documents. And when the day comes that a new kind of item lands in your workspace, you’ll be better prepared to guide it to the right place with confidence.

Final thought

Data-rich environments win over lean ones in the long run. So, next time you plan the workflow, ask not just how many documents you need, but how they can best represent the spectrum of work you’re helping organize. The answer will likely point you toward a richer, more reliable categorization approach, one that serves you and your team well for months to come.