Many organizations attempt to add Arabic AI by simply wrapping an English-centric LLM with Arabic prompts or runtime machine translation. While this produces superficially readable text, it frequently fails in production when handling dense domain-specific documents and terminology.
The Morphological and Chunking Challenge
Arabic is morphologically rich, with clitics, affixes, spelling variation, and optional diacritics that can affect retrieval and tokenization. Generic chunking rules that ignore sentence boundaries, document structure, or Arabic normalization can create weak retrieval units, especially in dense domain documents. The right strategy should be evaluated on the target corpus rather than assumed from English defaults.
Embedding Models and Hybrid Retrieval
Hybrid retrieval—combining dense semantic retrieval with lexical methods such as BM25, with Arabic-aware normalization where appropriate—can improve recall and relevance on many corpora, but the combination should be validated against a representative evaluation set.
Engineering Takeaway
At INTELLIFY, we build Arabic AI pipelines from the ground up: document cleaning, orthographic normalization, semantic chunking boundaries, and domain-grounded evaluation benchmarks to ensure high-accuracy responses.