The Classification Index is essential to establish before the prioritized review in an Active Learning project.

In Relativity, teams. are often juggling piles of documents, aiming to separate the signal from the noise with smart machine help. When you’re running an Active Learning project, one move comes before the first round of prioritized reviews: you create a Classification Index. It sounds like a small setup step, but it’s really the backbone that shapes how quickly you reach solid decisions and how accurately you separate what matters from what doesn’t.

Let me explain what the Classification Index actually does in plain terms. Think of it as a blueprint that organizes documents by how relevant they are, based on what your predictive coding model has learned so far. It’s not just a fancy label dump; it’s a living framework that translates model predictions into a practical workflow. With the index in place, you can see at a glance which documents are most likely to be important, which ones are less promising, and why those judgments were made. That clarity matters when you’re trying to move fast without sacrificing accuracy.

Why create the Classification Index first? Because it guides the entire prioritization process. Here’s the core idea: the index sets the criteria for what “priority” means in your project. It tells reviewers, “These documents are top of the list because the model believes they’re highly relevant.” It’s a kind of compass for the team, pointing attention to the items that will most influence the outcome of the review. Without this compass, you might end up chasing a moving target, reviewing a lot of material that doesn’t move the needle. With the index, you align effort with evidence, which usually translates into fewer hours spent on low-yield items and more time spent where it actually counts.

A quick mental model helps: imagine you’re curating a library for a complex research project. You don’t just toss every book onto any shelf and start reading blindly. You first sort by topic, priority, and potential impact, so the most relevant titles grab your attention first. The Classification Index operates the same way, but in a high-stakes, data-rich environment. It tells you which documents should be earmarked for early review because they’re most likely to inform decisions, given what the model already knows.

How the index interacts with other Relativity components

• Review Queue: The index feeds the queue with high-priority documents. Reviewers work through items in order of predicted importance, which helps speed up the learning loop—the phase where human insights refine the model’s accuracy.

• Coding Panel: As reviewers label documents, those codes feed back into the model. The Classification Index harmonizes these outcomes, ensuring that what gets labeled aligns with the defined priority scheme. In short, the panel and the index work in tandem, each learning from the other to tighten predictions.

• Document Library: The library is the home where all documents live. The Classification Index doesn’t replace it; it helps you navigate it more intelligently. By organizing documents based on relevance and predictive cues, you can locate critical items faster, tag them consistently, and preserve a clear trail of how decisions evolved.

A practical setup, step by step

• Step 1: Define priority categories. Before you touch a document, you should have a sense of what “high relevance,” “medium relevance,” and “low relevance” look like in your project. Decide the criteria that will map to those levels. It helps to keep categories simple at first—three or four well-defined buckets often beats a dozen fuzzy ones.

• Step 2: Establish the initial coding framework. What labels or codes will the team apply? How will those codes reflect the model’s predictions? Get these definitions documented and accessible so everyone uses the same language.

• Step 3: Seed the model with initial labels. A small, representative subset of documents labeled correctly provides the starting signal your classifier needs. The goal isn’t to be perfect on the first pass, but to give the model a foundation to learn from.

• Step 4: Build the Classification Index from the learning signals. Translate the model’s scores and the labels into a structured index that shows which documents sit at the top of the priority list and why. The index should be transparent enough that reviewers understand the rationale behind each item’s placement.

• Step 5: Populate the Review Queue with index-driven priorities. Let the queue guide the first round of human review. As reviewers work, capture feedback and update the index so it reflects new insights.

• Step 6: Iterate. Active Learning is iterative by nature. As the model refines its understanding, the index should evolve. Don’t hesitate to adjust categories, reweight certain signals, or revise criteria if you notice drift or inconsistencies.

Common pitfalls and how to avoid them

• Too many or vague categories. If the index has dozens of buckets or unclear definitions, reviewers may get confused instead of guided. Start simple, and expand only if you’ve locked down stable, meaningful criteria.

• Ambiguity in the criteria. If “relevance” means different things to different team members, the index will produce inconsistent results. Document criteria clearly and review them with the team.

• Not updating the index as learning progresses. The value of Active Learning comes from feedback loops. If you stop updating the index, you miss chances to improve accuracy and efficiency.

• Mislabeling during the seed phase. Early labels shape the model’s worldview. Use representative samples and a quick quality check to avoid teaching the model bad habits.

• Overreliance on automatic scores. The index is powerful, but it still benefits from human insight. Maintain human oversight to catch edge cases or misclassifications.

Analogies that stick

• It’s like planning a road trip. You map out the route (priority categories), set your GPS to highlight the next best turn (the Review Queue guided by the index), and adjust your plan as you go when traffic changes (iterative refinement of the index). It’s not flashy, but it’s incredibly effective when you want to reach the destination with fewer detours.

• Or think of assembling a team for a research project. You assign roles based on strengths (categories), then you review progress in a prioritized way so the strongest contributors shape the early outcomes. The Classification Index is the playbook that keeps everyone aligned without shouting across the room.

A few practical tips you can use right away

• Keep the indexing criteria tangible. Use concrete signals like document type, custodian, date range, or subject matter, along with model confidence scores.

• Start with a lean configuration. It’s easier to manage and adjust as you learn.

• Document decisions. A lightweight record of why certain items are prioritized helps onboarding new teammates and maintaining consistency.

• Revisit the index after major milestones. When you hit a new data subset or a change in scope, a quick index refresh pays off.

• Balance speed with accuracy. The aim is to accelerate the most crucial reviews without sacrificing the reliability of your results.

Real-world takeaways

If you’re mapping out how a Relativity project flows, the Classification Index is the quiet engine that keeps everything moving in the right direction. It gives you a clear map of what to tackle first, based on where the predictive coding model thinks you’ll get the most value. That clarity not only speeds things up but also reduces the guesswork that tends to slow projects down.

So, if you’re building a robust Active Learning workflow, start by crafting a thoughtful Classification Index. It may seem like a small step, but it sets the stage for smarter prioritization, tighter feedback loops, and a cleaner path from data to decisions. When you see the index in action, you’ll notice how much smoother the whole process feels—the queue fires up with purpose, reviewers stay focused, and the team moves more confidently together.

Final takeaway: The Classification Index isn’t a garnish on the project plan. It’s the foundation. Create it with care, keep it current, and let it guide the prioritization that follows. In a data-rich environment like Relativity, that order often makes all the difference between chasing quantity and delivering real insights.