Documents that introduce new concepts should be added before an incremental build for both conceptual and training data sources

Let me set the scene quickly. Think of a data system as a living library. Every new document is a potential new shelf, a fresh corner with its own stories, terms, and connections. When you run an incremental build in Relativity’s world, you’re not starting from scratch—you’re refreshing the shelves with items that can change how the entire library helps you navigate and predict. The big question to tackle here: which new documents deserve a spot on those refreshed shelves before you run the update?

New concepts take center stage

If you’re choosing documents to add before an incremental build, the clear winner is A: documents that introduce new concepts. Here’s why that matters, in plain terms. New concepts bring new vocabulary, new relationships, and new contexts that weren’t part of the system before. They expand what the model can understand and how it interprets existing material. Without fresh concepts, you risk leaving blind spots that the updated data can’t fill.

Think about it like this: imagine your data model as a map. Old concepts are the roads you already know well. New concepts are the new streets that suddenly open up—they connect neighborhoods you didn’t even know existed. If you only redraw the same roads, you might improve traffic flow a little, but you won’t discover new routes that lead to faster, smarter decisions.

What about the other options? Why not B or D, or even C?

• Documents that make up 10-30% of the population (option B) or less than 10% (option D) can still matter in other settings. But when you’re aiming to improve an incremental build, quantity without novelty doesn’t provide the lift you’re after. If a chunk of material repeats what’s already known, it’s like polishing an already spotless floor—nice, but not game-changing.

• Documents with no new concepts (option C) are precisely the kind of content you’d generally skim past during an update. They can bloats the processing time without expanding understanding. In practice, they’re the busywork that doesn’t move the needle.

A practical frame: novelty as the performance lever

The scenario you’re studying isn’t about dumping more data; it’s about strategic enrichment. New concepts act as catalysts: they prompt the model to reconsider assumptions, refine hierarchies, and adjust probabilities in informed ways. When you refresh with these documents, you’re teaching the system to map this broader landscape—lowering the risk of stale answers and vague predictions.

A closer look at why concept drift matters

Even if a document isn’t long, it can be a game changer if it introduces fresh terms or ideas. This is what practitioners call concept drift in lay terms: things change, and your model needs to recognize and adapt to those shifts. If a new regulation, a newly popular workflow, or emerging terminology shows up in a document, the incremental update should greet it with an updated understanding rather than treating it as noise.

A simple mental model to guide your selection

• If a document adds new terms, categories, processes, or relationships, it likely carries new concepts.

• If it rephrases old material but doesn’t introduce new ideas, it’s less critical for an incremental refresh.

• If it reflects familiar patterns with minor tweaks, you can schedule it for a later pass, when you’re ready to augment with other novelty-driven materials.

How to spot new concepts in practice

Here’s a straightforward way to assess documents without turning the process into a scavenger hunt:

• Term emergence: Do new terms or jargon appear that weren’t part of the existing vocabulary?

• Relationship expansion: Are there new connections between concepts—like a process that links two areas previously treated as separate?

• Context shift: Does the document place familiar ideas in a new setting, problem, or workflow?

• Topic novelty: Do topic modeling or embeddings reveal a cluster that isn’t well represented in the current data graph?

If the answer to one or more of these questions is yes, that document is a strong candidate for the incremental build.

A practical workflow you can try

• Step 1: Scan incoming material for novelty. Use lightweight NLP checks to flag documents with new terms or topics.

• Step 2: Tag new-concept documents. Give them a clear label so your data pipeline can treat them as high-priority during the update.

• Step 3: Validate relevance. Quick human checks or lightweight auto-validation can confirm that the new concepts fit within the model’s domain and won’t introduce noise.

• Step 4: Merge with care. Integrate new-concept documents into both conceptual and training data sources, ensuring that the update reflects the broader knowledge you’re building.

• Step 5: Run the incremental build. Monitor for improvements in accuracy, precision, or the ability to answer questions about newer topics.

A broader perspective: the data ecosystem, not just a single update

Relativity projects thrive when teams see data as a living ecosystem rather than a static pile. Incremental builds are like periodic checkups for this ecosystem. They’re most effective when you feed them fresh ideas that shift how the system sees the world. Documents that introduce new concepts do exactly that. They push the model beyond its comfort zone, inviting it to generalize better and infer new patterns from the updated knowledge base.

A quick digression: learning from other domains

You don’t have to be in one silo to appreciate the principle. Think about how a city grows: new districts bring different traffic patterns, utilities, and services. A smart city planner doesn’t just patch roads; they study where new neighborhoods are forming and how people flow through them. In data terms, new concepts are those neighborhoods. Ignoring them means you might miss the next surge of queries, the next edge case, or the next regulatory twist.

A gentle reminder about the boundary cases

Sometimes a document may introduce a concept that’s only marginally relevant or short-lived. In those cases, use practical judgment. If the concept has staying power and broad relevance across your datasets, it’s worth including. If it’s a one-off anomaly, you might want to catalog it for traceability but hold off on a full integration until you see longer-term utility.

A concise checklist you can keep handy

• Does the document bring a term or idea that isn’t in the current data set?

• Does it reveal a new relationship between topics, processes, or data types?

• Is the concept likely to appear in future materials or inquiries?

• Will adding it improve your model’s ability to explain or predict outcomes in broader scenarios?

• Can you verify that including it won’t introduce noise or misalignment?

If you answer yes to the first two questions, you’ve probably found a good candidate for inclusion before the incremental update.

A note on tone and approach

In real-world teams, the rhythm of data work is a mix of rigor and pragmatism. You want to be precise about what you add, yet you don’t want to get lost in endless gatekeeping. The goal is to keep the data living and relevant, not to drown it in sameness. This balance—between thoughtful novelty and practical restraint—helps you move faster without losing trust in the results.

Turning insight into action

The core takeaway is straightforward: for an incremental build, prioritize documents that introduce new concepts. They are the fuel that updates the system’s understanding in meaningful ways. Other materials—while sometimes informative—don’t push the model into new ground. By focusing on novelty, you’re more likely to see clearer improvements in how the model navigates questions, draws connections, and offers guidance.

A closing thought

If you’ve ever rewritten a rulebook, you know how small changes can unlock big improvements. A new concept in your data is that subtle yet powerful difference—the spark that makes a familiar landscape feel fresh and more navigable. In the grand scheme of data management, that spark is worth seeking out and prioritizing during an incremental refresh.

In case you’re curious about the practical underpinnings: think of contemporary data pipelines that handle conceptual and training data sources as two intertwined streams. The conceptual stream guides understanding—how concepts relate, what terms matter, and where the gaps lie. The training stream tests and reinforces that understanding, ensuring the model can apply what it has learned to new questions. When you feed both with new-concept documents, you’re aligning knowledge with reality—keeping the system current, capable, and dependable.

So, the next time you’re planning an incremental build, ask the simple, powerful question: does this document bring something new to the table? If the answer is yes, give it a place on the refreshed shelves. If not, set it aside with a note for possible future inclusion. Your future self—and the people who rely on the system—will thank you for the clarity and the nimble adaptability that follows.