Start Review is the button that begins coding documents from the document queue in an Active Learning project

Start Review: The Simple Button That Starts the Learning Loop

Let me ask you something. When you’re navigating a big stack of documents in Relativity, what tiny action signals you’re ready to roll up your sleeves and actually label the content? If you’re in an Active Learning project, the answer is a single, almost unglamorous click: Start Review. That button isn’t just a label—it’s the green light that kicks off the coding phase from the document queue. And yes, it matters. A lot.

What Start Review actually does

Here’s the thing about Active Learning workflows: they’re built on a subtle partnership between human judgment and smart software. You don’t just flip a switch and hope the model understands. You start the review, and your first labels feed the system, helping it learn what kinds of documents matter, what topics to surface, and how to tag things correctly. The Start Review button is the moment you say, “I’m ready to judge these documents, and I’m contributing my insights to guide the model.”

Think of it as the moment you stop surveying and begin acting. You’ve scanned the queue, you’ve formed a plan, and now you commit to coding. That commitment—via Start Review—sets the tempo for the whole session. It’s where the human-in-the-loop element becomes the engine that powers faster, more accurate results over time.

A quick tour of the other options (so you don’t mistakenly press the wrong one)

Relativity’s interface offers several commands that look tempting, but they don’t start the coding process in the same way. Here’s how the common choices differ, so you can pick with confidence:

• Check Out Batch: This is about grabbing a chunk of documents for your own work session. It’s handy if you’re organizing your day or avoiding other reviewers stepping on your toes. It does not, by itself, begin the actual coding in the Active Learning loop.

• My Assigned Documents: Think of this as your to-do list. It shows what you’re responsible for, but it doesn’t boot you into the act of coding. It’s a planning view, not the start of the analysis.

• Start Coding: This sounds right, but in many setups the first decisive action isn’t coding yet. It might prompt you to begin tagging or to choose a coding scheme, but the workflow typically requires you to start the flow with Start Review to officially enter the document queue and let the model begin learning from your inputs.

So the correct move in most Active Learning projects is Start Review. It’s the moment you transition from reading and preparing to actively labeling, which is what drives the learning loop and, ultimately, results that matter.

Why this button is more than a convenience

You might wonder, “Why not just start coding whenever you feel ready?” The answer is that Start Review helps keep sessions aligned with the project’s learning goals. When you press Start Review, you signal several things at once:

• Intent: You’re ready to code and to provide consistent labels.

• Rhythm: The system expects your input as part of a training cycle. Your actions influence model suggestions, which, in turn, shape future reviews.

• Accountability: A clear start point makes it easier to track progress, compare results, and audit decisions if questions pop up later.

That alignment matters because Active Learning isn’t only about speed; it’s about quality over time. A thoughtful first pass sets a strong foundation for the model to learn from, which pays dividends when you’re working with large document sets or tight timelines.

A practical look at the flow

If you’re in the groove, the Start Review moment feels almost automatic. Here’s a simple mental map of what happens next:

• You enter the document queue as a reviewer. The system presents you with a batch of documents that need evaluation.

• You read, assess, and code each document according to the project’s coding taxonomy—things like topics, metadata fields, rubric-based labels, or issue tags.

• Your feedback feeds a learning model. The model uses your labels to refine its own suggestions for similar documents in the queue.

• You move through batches, refining your approach as the model adapts. The more you label consistently, the smarter the model gets.

• You publish or save your session, depending on how the workflow is configured. The data you’ve produced becomes part of the growing knowledge base that guides future review rounds.

This is where the blend of human judgment and machine efficiency shines. You don’t just stamp documents as done; you train a system that helps you do the rest faster, with fewer blind spots.

Tips for a smooth Start Review experience

Here are a few practical ideas to keep your Start Review sessions sharp and steady. They’re quick to implement and they pay off when you’re in the thick of a long review.

• Set a small, consistent coding routine: pick a tagging approach you can repeat across documents. Consistency matters more than cleverness here.

• Use clear, well-defined labels: if your taxonomy is fuzzy, the model will get confused. Clear labels help the machine learn and you stay aligned with the project goals.

• Review in manageable chunks: don’t try to sprint through hundreds of documents in one go. Short, focused sessions reduce fatigue and mistakes.

• Check your work against a quick rubric: a mini-checklist at the end of a batch helps catch off-pattern labeling before you move on.

• Leverage the model’s suggestions, but don’t default to them: see why the system suggested something, then decide whether your judgment confirms or overrides it. That’s the sweet spot where learning happens.

• Save frequently: it’s easy to lose momentum if the app hiccups. Regular saves keep your hard work intact.

• Use filters and search strategically: narrow down the queue to relevant doc types, dates, or custodians so you’re not overwhelmed by noise.

• Keep a light audit trail: jot down a sentence about tricky cases or edge conditions. If a doc sparks questions later, you’ve got a ready reference.

Relativity in everyday terms

If you’ve ever organized a big family project or coordinated a school club, you know that a good plan folds in a little flexibility. Start Review is your signal to begin that plan in a professional setting. It’s like setting the table before a family meal: you lay out the plates, you adjust for tastes, you prepare the space so everyone can contribute smoothly. The difference here is that your contributions have a concrete, machine-enabled ripple effect—each label trains a better model, which makes future reviews quicker and more accurate.

A few caveats to keep you grounded

No system is perfect, and even the best workflows have rough edges. Here are some common snags and how to handle them without losing momentum:

• Fatigue bias creeps in after long sessions. Take short breaks and rotate between reviewers if possible. Fresh eyes help maintain consistency.

• Inconsistent labeling across batches. When you notice drift, pause, review your taxonomy, and realign with the team.

• Overreliance on model suggestions. Treat them as aids, not final authorities. The human touch is where precision lives.

• Queue churn can feel chaotic. Use the “Check Out Batch” option strategically to carve out focused windows for deep work, then return to the main queue with renewed clarity.

A quick distinction you can carry forward

Here’s a simple mental shortcut you can use in the moment: Start Review is the trigger to begin coding from the document queue. Check Out Batch is about securing a chunk of work for a focused session. My Assigned Documents is your roster view, and Start Coding is a cue that you’re moving into labeling, but the official entry into the active learning loop is Start Review. Remembering this helps you navigate the interface without losing your place in the process.

Wrapping up with a human touch

Relativity’s Active Learning setup sits at the crossroads of intent and automation. Start Review is the door you open to initiate that collaboration. It’s less about a single action and more about embracing a workflow—one where your careful judgments teach a system to do more of the heavy lifting, while you keep the ship pointed in the right direction.

If you’ve sat with a document stack lately, you know the feeling: a mix of curiosity, responsibility, and a touch of urgency. The Start Review moment captures that mix and gives it a practical outlet. It’s small, it’s straightforward, and in the right rhythm, it changes the whole game.

A final thought: the best reviewers treat this start not as a hurdle but as a doorway. Press it with purpose, stay steady, and you’ll notice the difference—not just in how fast you move, but in how clearly you see the path ahead. Start Review isn’t just a button; it’s the first step in a collaborative journey between human insight and machine learning, one that keeps getting better the more you contribute.

If you enjoyed the practical vibe of this breakdown, you’ll find that the Relativity ecosystem rewards thoughtful interaction and steady, consistent labeling. And that’s something worth leaning into, from first click to the last document in the queue.