TL;DR

A practical guide to evaluating dialect prompts, Arabizi, code-switching, retrieval quality, tone, and multilingual fallbacks before deploying customer-service AI.

The Complex Web of Arabic Dialects (Not Just Saudi Arabia)

Arabic includes Modern Standard Arabic and many regional dialects. A useful evaluation should include Egyptian, Levantine, Gulf, Iraqi, and Maghrebi prompts where relevant to your customers. Model behavior varies by prompt, knowledge source, and configuration, so native speakers should review accuracy, tone, and escalation rather than assuming universal dialect coverage.

1. Handling Real-World Slang, Typos, and Mixed 'Arabizi' Input

Customers use slang, spelling variation, code-switching, and Arabizi (for example, '3alekom elsalam, sh7alekom?'). Include these patterns in your test set, record misunderstood intents, and add deterministic clarification or human handoff for requests the model cannot identify confidently.

2. Test direct Arabic processing and translation-based designs

Arabic morphology and mixed-language input can challenge retrieval and generation systems. Ask vendors whether retrieval, embeddings, reranking, and generation handle Arabic directly or through translation. Test both approaches with your documents; neither architecture guarantees perfect answers, and sensitive replies should always have a human fallback.

3. Validate each language you plan to support

Available language quality depends on the selected models and configuration. Test English, French, Urdu, Hindi, Tagalog, or other required languages separately; do not infer quality from a language-count claim. Include language detection errors, mixed-language conversations, document retrieval, and fallback wording in acceptance testing.

4. Build a repeatable localization scorecard

Score intent accuracy, factual grounding, tone, response time, safe refusal, and human handoff for each target language. Run the same prompts across shortlisted platforms and repeat the test after model or knowledge-base changes. Choose on measured fit, not superlatives.