Better search starts with the context of your documents

“The agreement renews automatically after twelve months.”

For someone reading the full contract, this sentence may be perfectly clear. A search system also needs to know which customer, which agreement and which version it belongs to. Those details might appear several pages earlier.

This creates a familiar problem in company knowledge: a useful passage can lose its meaning when separated from the document around it.

Preserving context in the search index

In Context is Gold to find the Gold Passage, Max Conti and colleagues introduce ConTEB, a benchmark for retrieval that requires document context. Their method, InSeNT, combines contextual embedding training with late chunking: passages receive information from the surrounding document before their individual representations are formed. The authors report improved retrieval on ConTEB while preserving base-model performance.

The practical question this raises is straightforward: how much useful context can we preserve when preparing documents for search?

That preparation matters before a language model writes an answer. Context included in the search representation can affect which passages are retrieved in the first place. Context added only after retrieval helps the answering model interpret the passages already selected.

Start with information the document already provides

For a business document, useful context often exists in structured form:

• The document title and section heading.
• The customer or project it belongs to.
• The document type, version and date.

Consider a passage indexed together with “Northwind — service agreement — 2026 — renewal conditions.” A question about Northwind’s renewal terms now has explicit information to match against.

Adding these fields does not require generating a separate explanation for every passage. It does require consistent extraction and careful selection: irrelevant or repeated metadata can make passages harder to distinguish.

This is a straightforward baseline to test alongside contextual embedding methods. It is a separate approach from InSeNT, with its own quality and cost trade-offs.

Measure whether the right evidence arrives

For Traize, a useful comparison would start with real company questions and the passages needed to answer them. We would compare ordinary chunks, chunks with structured context and contextual embeddings.

The key measures are retrieval quality, indexing cost and the time required to keep changing documents up to date. We would also examine whether improved retrieval reduces follow-up searches during answer generation.

The customer benefit is concrete: finding the relevant clause, decision or project update sooner. Preparing company knowledge well gives the rest of the AI system a stronger starting point.