Which documents should be added to a conceptual index before an incremental build?

Let’s talk about a detail that often slips through the cracks when teams manage large document collections in Relativity: the way you feed a conceptual index data source before you run an incremental build. The short version of the rule is surprisingly simple, yet powerful: add documents that don’t introduce new concepts. Yes, you read that right. We’re strengthening what already exists, not broadening it with fresh ideas right away.

A quick mental model first: what is a conceptual index anyway?

Think of a conceptual index as a map of ideas, terms, and relationships that a project team has decided are the core landmarks in a dataset. It’s not just about counting words; it’s about understanding what those words signify and how they connect. The concept dictionary is built from feed-forward knowledge—terms you’ve already agreed matter—and the relationships among those terms. An incremental build uses that map as a backbone, layering in updates without reworking the entire structure from scratch.

Now, why would you want to add documents that contain no new concepts before you run that incremental pass?

• Clarity over clutter. When you drop in documents that reinforce existing concepts, you’re reinforcing the current map. You’re not muddying the waters with brand-new semantics that might require rethinking how the map should look. It’s like tightening the screws on a cabinet you already know how to assemble.

• Speed over noise. Incremental builds should be quick and predictable. If you pull in documents that introduce new ideas, the system has to re-evaluate relationships, adjust the concept graph, and possibly reindex large swaths of data. That slows things down and can introduce small, hard-to-detect inconsistencies.

• Stability over disruption. Existing concepts carry familiarity. Reinforcing them keeps the indexing process stable, which makes downstream tasks—like searching, filtering, and reporting—more reliable.

What counts as “no new concepts” in practice?

A document qualifies when its content largely echoes what’s already in the concept dictionary. Here are some concrete examples:

• Policy updates that restate existing concepts. The policy text might be updated for clarity, but the core ideas and terms stay the same. In the index’s eyes, you’re polishing the same furniture, not adding a new room.

• Standard forms and templates with familiar vocabulary. A contract template that continues to use the same definitions and terminology is a perfect candidate. The form gets updated, but the semantic footprint doesn’t grow.

• Redlined documents that fix wording without adding new meaning. If a redline changes phrasing but retains the same concepts and relationships, it’s reinforcing the current map rather than expanding it.

• Metadata-only changes that don’t alter subject matter. If a file’s metadata shows a new owner or a status update but the substantive content remains within existing concepts, it belongs in the reinforcing bucket.

What doesn’t fit the rule?

There are two easy traps to avoid:

• Documents that introduce new concepts. Anything that introduces new terms, new relationships, or new domain ideas shifts the concept map. Even if a doc is short, new terminology can ripple through indexing decisions, requiring updates to the concept dictionary and possibly changing how related terms are surfaced.

• Documents that barely touch the subject matter but are still loaded with new ideas. A long memo that mentions several new ideas, even if only a minority of the text, can still expand the concept space. Unless you’ve earmarked a separate pass to assimilate those new ideas, it’s safer to treat such content as a candidate for a broader indexing cycle, not the immediate conceptual refresh.

A practical workflow to apply this idea

1. Preflight thought experiment: concept delta analysis. Before you push anything into the conceptual index data source, run a quick delta check. Does the document introduce terms or relationships not present in your current concept dictionary? If yes, flag it for review as a potential candidate for a future pass (or for inclusion via a different data source or workflow).

2. Tag and filter. Create a clear tag for documents that contain no new concepts. This makes it easy for the indexing team to assemble the subset needed for the incremental build and keeps the rest of the data pipeline organized.

3. Reinforce the map. Add the “no new concepts” documents to the conceptual index data source. They’ll tighten the existing framework, rather than widening it prematurely.

4. Run the incremental build. With a clean, concept-stable set, the incremental pass is leaner, faster, and more predictable. You’ll also see fewer surprises in search relevance and error rates.

5. Validate and close the loop. After the build, check a sample of results to ensure that the reinforcement didn’t mask any critical new context. If something looks off, mark it for a deeper dive in a subsequent cycle.

A quick mental model for decision-making

• If the doc’s essence is already captured in the concept map, it belongs in the reinforcing batch.

• If the doc brings something genuinely new to the table, set it aside for a future, targeted update so it can be integrated without destabilizing the current index.

• If you’re unsure whether a doc contains new concepts, lean on a lightweight review: skim for unfamiliar terms, new definitions, or novel relationships. When in doubt, err on the side of separation—it’s easier to adjust later than to unwind an overgrown concept graph.

A few tips to keep your Relativity PM workflows smooth

• Build a living concept dictionary. Treat it like a product backlog for terminology. As new terms pop up in your data, capture them with context and cross-reference to existing concepts. This makes future updates more efficient.

• Use term frequency as one signal among several. Frequency alone isn’t a perfect measure of a concept’s importance, but it helps surface areas where reinforcement is especially valuable.

• Keep a clear separation of concerns. Use separate data sources or folders for documents that reinforce existing concepts versus those that introduce new ones. That separation makes the incremental build more transparent and manageable.

• Don’t chase perfection in one pass. It’s common for a few documents to have ambiguous impact. Schedule a deliberate review cycle for edges that aren’t clearly new or old, so you don’t get stuck in analysis paralysis.

• Leverage Relativity features. When available, use the platform’s analytic tools to spot concept drift, term evolution, and relationship shifts. Those signals can guide which documents to reinforce next.

A note on the nuance

There’s a subtle tension here that’s worth naming. You might encounter a document that, on the surface, doesn’t introduce new concepts but changes the way existing concepts relate to each other. For example, a memo that clarifies how two concepts interact, or tightens a definition’s boundary. Even in such cases, you’re not adding new atoms to the concept map; you’re refining how those atoms connect. In the incremental build world, that’s still a reinforcement move, but with a closer watch on relationships that shape relevance and retrieval.

Relativity PM Specialist topics in everyday work

If you’re operating in teams that rely on this kind of indexing discipline, you’ll recognize how these decisions echo through day-to-day tasks. The indexing team coordinates with the data engineering group to ensure the data sources stay aligned with the evolving knowledge map. Stakeholders from discovery and legal teams may rely on consistent results when searching across matter repositories. In practice, that means a smoother review workflow, fewer false positives, and an easier path to uncover the right documents fast.

Why this approach makes sense in real-world projects

In many digital projects, information floods in from a dozen different channels. People update cases, policy files get revised, templates get tweaked. Without a disciplined, concept-focused intake, you end up with a sprawling index that’s harder to navigate and slower to respond to. The strategy of adding only documents that don’t introduce new concepts helps preserve a clean, navigable framework. It’s not about freezing data; it’s about guiding growth so that every incremental improvement builds on a solid, understood base.

Bringing it all together

Relativity’s conceptual index data source is a powerful ally when you’re stitching together vast collections of documents. The trick is to let the incremental build work on a well-worn map first—documents that reinforce what you already know—before you invite new ideas into the map itself. It’s a steady rhythm: reinforce, validate, and only then expand the concept space. When you approach indexing this way, you gain predictability, speed, and clarity—three things that make any project team more confident about search, discovery, and decision-making.

If you’re exploring how this approach plays out in your own Relativity environment, start with a small pilot. Pick a matter with a stable concept set, run the delta analysis, and compare the incremental build outcomes with and without the reinforcing documents. You’ll likely notice the difference in speed and consistency. And once you’ve seen that, the method tends to spread—quietly, reliably—across other matter streams.

Curious about the broader landscape? A good place to start is to map your concept dictionary to the data sources you manage. Build a living glossary of terms, definitions, and relationships, and treat new terms as a signal that calls for a separate, focused update. In the end, your conceptual index becomes less about throwing everything at once and more about thoughtful, purposeful growth.

In short: when you’re prepping for an incremental build, pick the path that strengthens what you already know. Documents with no new concepts don’t complicate the map; they refine it. And a refined map is what makes search crisp, navigation intuitive, and insights faster to reach. That’s the kind of approach that keeps Relativity environments humming—efficient, reliable, and ready for the next piece of the puzzle.