Why you might not see 200 documents when you first open the Active Learning queue

Outline I. Setting the scene: the myth about 200 documents in Active Learning II. What actually drives what you see in the queue III. How Active Learning queues behave in Relativity (in plain terms) IV. Practical steps to understand and manage your queue V. Common scenarios and quick tips VI. Wrapping up: the takeaways and how to stay in sync with the data

Active Learning queues in Relativity aren’t a crystal ball that hands you a fixed pile of files the moment you sign in. The statement that you’ll see 200 documents right away isn’t a universal rule. In fact, the initial display is shaped by several moving parts. If you’re looking to understand what shows up—and why—the answer isn’t a single checkbox. It’s a little orchestra of permissions, data status, and system settings. Let me walk you through how it actually works, so you’re not left wondering what’s going on after you log in.

What actually determines what appears in the Active Learning queue

Think of the queue as a space that reflects what your project can access, what has been ingested, and how filters are set up. The moment you open it, you’re not guaranteed a certain number of documents. Here are the main influencers:

• Permissions and access: Your role inside Relativity matters. If you don’t have rights to view certain folders or custodians, those documents won’t appear in your queue. It’s not about trying to be sneaky; it’s about keeping sensitive data protected and ensuring everyone sees only what they’re allowed to see.

• Data availability: If the dataset hasn’t been ingested yet or if a portion of it is temporarily unavailable, the queue can be smaller—or even empty. In other words, a blank slate is possible, not a bug, just a reflection of what’s actually in the system at that moment.

• Configuration and filters: Most teams fine-tune the Active Learning setup with filters, seed sets, or view-specific criteria. If your filters are tight, you’ll see fewer documents. If they’re broad, you might catch more. It’s a bit like setting the dials on a radio to hear just the stations you care about.

• Ingest status and processing queues: If documents are still in the ingest pipeline, or if a job is running to label or tag content, those items may not be ready for the Active Learning queue yet. You might not see them until the processing steps complete.

• Project-specific rules: Some environments have rules about when and how items are released to different parts of the workflow. A document might be ready in the background but not yet visible to a particular user or team until a certain stage is reached.

• User interface and session state: Occasionally, a quick refresh or a new session can affect what you’re shown. If a system hiccup happens, you might momentarily see fewer items until things settle.

How the Active Learning concept translates in Relativity

If you’re tuning into what Active Learning is doing, here’s the down-to-earth version: the tool helps you work with a set of documents and an ongoing feedback loop. You label some items, the model tunes its guesses, and the process pushes more relevant material toward you. It’s not a one-and-done stream; it’s iterative and depends on the current data, your actions, and how the system is configured.

Because the queue is tied to your workspace, your access, and the dataset, it behaves like a living shoreline. It shifts as new documents arrive, as permissions change, or as your filters are adjusted. It’s not about chasing a big number. It’s about ensuring you’re looking at what matters most, given the controls you have.

Practical steps to understand and manage your queue

If you want to get a clear, steady sense of what to expect, here are simple checks and habits that help:

• Confirm your permissions: If you’re unsure whether you should see certain folders or custodians, double-check your access rights. Sometimes the source of a small queue is simply restricted permissions. No mystery here—just a reminder to align access with the task at hand.

• Review the filters and criteria: Take a quick look at the active filters. Are you targeting a particular date range, custodian group, or document type? A small tweak can dramatically change the queue size. Think of it as adjusting your search lens.

• Check data ingest status: If you’re waiting on new materials, peek at the ingest or processing indicators. If documents are still in motion, they may not be visible yet. Patience here isn’t laziness; it’s realism that data needs to settle.

• Refresh and re-evaluate: A quick refresh can resolve a lot of oddities. If something seems off, a fresh load often restores the expected view. It’s a small ritual that helps you stay aligned with the current state.

• Start with a sensible seed set: If your goal is to train a model or kick off a review, begin with a manageable seed. It’s easier to gauge how the workflow responds and then scale up when you’re ready. No need to start big if you’re still learning the rhythm.

• Monitor and adjust: Active Learning isn’t a “set it and forget it” feature. Check in after a cycle, review which documents were added, and refine your filters or seed strategy accordingly. A little tuning goes a long way.

• Document the context: Keep notes on why you adjusted filters or changed permissions. It helps teammates understand the queue’s behavior later and reduces confusion when someone else opens the same project.

Common scenarios you might encounter

• Scenario A: You log in and see a handful of documents. That’s not unusual. The system is respecting your access and the current ingest status. You can still begin labeling and let the model learn from those items.

• Scenario B: The queue is empty for you, but a colleague with broad access sees a larger set. That’s a permissions cue. It’s a gentle reminder that visibility isn’t universal by design.

• Scenario C: You expect a pile of 200 documents, but you see only 50. Here, filters, processing status, or a stricter view might be the culprits. A quick review of the active criteria usually clears it up.

• Scenario D: A burst of new items appears after a data release or a pipeline completion. This is the moment when the queue grows in a meaningful way. Great time to re-check your seeds and start labeling again.

A few tips that stick

• Don’t assume a fixed number. The initial display isn’t a promise of quantity; it’s a snapshot of what’s currently accessible and ready.

• Treat the queue as a live workspace. It’ll evolve as new data comes in, as people adjust views, and as processing completes.

• Use small, iterative steps. Start with a subset, learn how your changes affect visibility, then expand.

• Communicate changes. If you adjust filters or permissions, a brief note to teammates helps everyone stay aligned.

Why this matters beyond the moment

The way the Active Learning queue behaves isn’t just a nerdy detail. It affects how quickly you can review material, how effectively you can train or fine-tune models, and how confidently you can interpret results. If you expect a monster dump of documents but get a sparse or blank view, you might rethink your seed strategy, your permissions, or your data readiness. It’s not a failure; it’s a signal that you’re looking at a dynamic system that rewards clarity and deliberate setup.

A practical mindset for working with Active Learning queues

• Start with transparency: Make sure your team openly discusses what each filter means, why permissions are set a certain way, and how data is ingested. When everyone understands the rules, the queue becomes a shared instrument rather than a confusing silo.

• Embrace the rhythm: The queue’s size will ebb and flow with data. Expect that—and plan your workflow around it. Some days you’ll label a lot; other days you’ll map out the data landscape and wait for the next wave.

• Stay curious but grounded: It’s tempting to chase a big count, but bigger isn’t always better. Relevance and quality of the documents you process matter more than sheer volume.

• Lean on the tools you know: Relativity provides signals—ingest status, permission indicators, and filter settings—that help you interpret the queue. Use those signals rather than guesses to guide your actions.

A closing thought: it’s okay to have a blank slate

If you ever log in and see no documents, take a breath. A blank slate isn’t a failure; it’s a truthful starting point. It says, “Here’s what’s ready to work with right now.” As you adjust seeds, broaden filters, or finish an ingestion, the queue will respond. The dynamic nature of the workspace is actually a strength—it means you’re not guessing what’s there; you’re seeing what’s accessible, and that makes your work more precise in the long run.

In the end, the initial display of the Active Learning queue isn’t a fixed number. It’s a reflection of access, data readiness, and system config—an honest snapshot rather than a guaranteed tally. With a little mindful setup and steady checking, you’ll ride the currents of the queue rather than feel swept along by them. And that, more than anything, keeps your project moving with clarity and confidence.