Some of your RAG-related issues have an easy & quick solution: decompounding

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Most teams working with Elasticsearch, OpenSearch or RAG pipelines focus on ranking, embeddings or model quality when trying to improve relevance.

But in many cases, the issue starts much earlier: in how text is normalized before indexing.

In a previous post, we looked at lemmatization as a way to reduce noise introduced by stemming. Here, we focus on another critical and often overlooked issue: compound words.

In languages such as German, Dutch, Swedish, Finnish, Korean and, in a different way, agglutinative languages such as Turkish, compound words can hide meaning from search engines and RAG systems.

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Why compound words break search relevance

From a linguistic point of view, decompounding is part of proper normalization for compound-heavy languages.

If compound words are not split correctly, one or more meaningful words remain hidden inside a single token. As a result, the search engine cannot match terms that are obvious equivalents to the user.

However, most language analyzers rely heavily on spaces to tokenize text. That means:

• USBCKabel is treated as one token
• USB C Kabel is treated as multiple tokens
• USB-C-Kabel may be split differently depending on analyzer configuration

The meaning is the same for the user, but not necessarily for the search engine.

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Why stemming alone is not enough

Stemming operates on tokens. If a compound word is treated as one token, the stemmer cannot properly normalize the words inside it.

In other words:

This creates recall gaps and forces teams to compensate later with more complex query logic, fuzzy matching, n-grams or semantic layers.

But the underlying problem remains the same: meaningful words are hidden before indexing even begins.

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The impact on semantic search and RAG

Many teams assume semantic search or embeddings will solve this problem. They can help, but they do not remove the need for good linguistic normalization.

Embeddings are generated from text. If important words are hidden inside compounds, the input representation is less complete than it should be.

This can affect:

• semantic retrieval quality
• lexical matching
• RAG grounding
• multilingual search consistency

In RAG systems, retrieval quality is critical. If the right document or passage is not retrieved, the generation layer cannot fix the problem.

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A better approach: decompound before indexing

The solution is to apply decompounding before indexing, as part of the normalization layer.

Once the compound is split correctly:

• each meaningful term becomes visible to the analyzer
• lemmatization can be applied correctly
• equivalent expressions can be matched more reliably
• indexing, retrieval and RAG pipelines receive cleaner input

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Fixing relevance at the source

A lot of teams treat retrieval issues as ranking, embedding or generation problems.

Often, they are not.

Sometimes the problem starts much earlier, with compound words that hide meaning from the system.

Using decompounding technology that splits compounds into correct words and then lemmatizes them preserves meaning for indexers, search engines and RAG pipelines.

In many cases, improving normalization upstream is one of the simplest ways to improve relevance downstream.

For a deeper understanding of how normalization impacts search relevance, check out this related post on
lemmatization vs stemming.

If you’d like to learn more or test this approach in your Elasticsearch or OpenSearch setup, feel free to
contact us here.