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A reliable Gulf-business test set starts with several hundred anonymised messages from the company’s own WhatsApp inbox, not a pile of translated examples. Label every case for script, language, dialect, intent, entities, expected action and safety outcome; add matched variants, context-dependent replies and voice notes; then rerun the set after every prompt, model or template change. This design measures whether an assistant understands customers and makes the right operational decision, including handing a conversation to a person when confidence is low.
What the test set must prove
A mixed-language WhatsApp assistant has two separate jobs: understand what a customer means and take an action that is safe for the business. A fluent answer in the wrong language, a misread order number or a missed escalation can be more damaging than a brief delay. Ujjwal Dubey’s practical guidance captures the priority: “A wrong confident reply in the wrong language costs more than a short wait.”
Define success as an operational contract before writing test cases. For each message or conversation, specify the language handling, intended intent, entities that must be extracted, allowed response language, policy constraints and expected disposition. The disposition should be one of a small controlled set such as answer, request_missing_information, refuse, or route_to_person. If the expected disposition is route_to_person, passing requires an actual handoff signal, not merely a sentence that suggests contacting support.
Set a baseline from real traffic
Sample several hundred anonymised messages from the business’s own inbox. Preserve the distribution of channels and customer journeys instead of selecting only clear, polite examples. Include sales questions, delivery or appointment changes, payment issues, complaints, policy requests, greetings, abandoned conversations and messages that arrive after a long pause. Record the sampling period and the number of cases in each slice so later results can be compared with the same baseline.
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No universal Gulf-wide percentage of mixed-language messages or universal accuracy threshold has been established. Your organisation’s inbox is therefore the defensible reference point. Report slice counts and, where practical, confidence intervals rather than presenting one aggregate score as a guarantee.
Design the case record
Give every case a stable ID and keep the original text separate from annotations. A compact schema makes the set reviewable in a spreadsheet, database or repository.
| Field | What to store | Example |
|---|---|---|
| case_id | Immutable identifier | UAE-2026-00481 |
| message_text | Original customer wording after approved redaction | Arabic script, English, or both |
| language_spans | Language for each span, not only one label for the message | Arabic script, English product name |
| script_form | Arabic script, Latin-letter Arabic (Arabizi), Latin-letter English, or mixed | Arabizi plus English |
| dialect | Known dialect, MSA, English variety, or unknown | Gulf Arabic |
| intent | Business action the customer seeks | Change delivery time |
| entities | Dates, amounts, IDs, locations and other required values | “22/10”, “Al Ain” |
| expected_action | Answer, clarify, refuse, or hand off | route_to_person |
| reply_language | Language or bilingual policy expected in the response | Follow customer’s dominant language |
| context | Prior turns needed to interpret a short message | Previous turn asked for confirmation |
| risk_notes | Privacy, payment, regulated or safety concern | Do not disclose account details |
| source_modality | Text or voice note, with an audio reference when applicable | WhatsApp audio clip |
Store annotations in a version-controlled repository with access controls. Keep a mapping from case IDs to the source conversation in a restricted system; the evaluation copy should contain only the minimum data needed to reproduce the test.
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Cover the language combinations customers actually use
Arabic script, English and Arabizi
Create separate slices for Arabic script, English, Arabizi (Arabic written with Latin letters and numerals), and messages that combine them. Treat Arabizi as Arabic for routing and response-language policy, while preserving the original spelling for recognition tests. Do not “correct” customer text before measuring understanding; a normalisation copy can be evaluated as a separate preprocessing experiment.
Include spelling variation deliberately: omitted vowels, repeated letters, phonetic substitutions and common chat abbreviations. Pair a noisy form with a clearer paraphrase of the same request so you can tell whether a failure is caused by wording or by intent coverage.
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Dialect and code-switching
Mark Gulf Arabic, Modern Standard Arabic, Egyptian Arabic, English and unknown when annotators can support the distinction. Within one message, label language spans rather than forcing a single language. Build matched variants in which only the language changes: for example, an appointment request in English, Arabic script, Arabizi and an Arabic-English blend. Add intra-message switches at the point where customers naturally switch, such as a product name, address, model number or a sentence-ending confirmation.
Code-switching is not an edge case to hide in a “miscellaneous” bucket. The public Mixat Emirati-English speech dataset contains 15 hours of audio, 5,307 sentences and reports that 36% of its sentences are code-switched. Its publication describes shortcomings in recognising low-resource Emirati dialect and the additional difficulty introduced by switching languages. Those figures describe that dataset, not the prevalence in your inbox, but they show why a Gulf deployment needs a dedicated slice.
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Pair Arabic-Indic and Western digits for every workflow that depends on numbers. Test dates with day-month and month-day ambiguity, times with and without a time zone, decimal separators, phone numbers and order IDs. Preserve numerals in the expected output when the business system requires an exact value.
Build alternate spellings for locations, malls, districts and landmarks. Include transliterations, abbreviations and forms with or without spaces. An assistant can identify the right intent yet still fail when the extracted location does not match the fulfilment system’s accepted value, so score extraction independently from intent classification.
Short replies that need conversation context
Include replies such as “yes”, “tomorrow”, “same one”, “لا”, “تمام” and one-word confirmations only with the preceding turns that make their meaning clear. Test both the expected continuation and a deliberately ambiguous context. The correct result for an ambiguous reply may be a clarification or a handoff; guessing should be scored as an error even when the guessed option happens to be valid.
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Build the set in a repeatable workflow
- Define the intent inventory. Start with the actions supported by the WhatsApp workflow and list forbidden or human-only actions. Give each intent a written boundary and at least one example of a neighbouring intent.
- Sample and redact. Export a time-bounded inbox sample, remove names, phone numbers, addresses and payment data according to the organisation’s policy, and retain a reversible link only in a restricted store.
- Stratify the sample. Set minimum counts for language form, dialect, modality, intent, risk level and conversation length. Keep a natural-distribution split as well as a stress split for rare but consequential cases.
- Annotate independently. Have two trained annotators label a portion of the set, reconcile disagreements and document decisions for Arabizi, dialect boundaries, entity formats and escalation triggers.
- Add matched variants. Rewrite the same intent across scripts, languages, spelling styles, number systems and levels of politeness without changing the expected action.
- Attach context. For multi-turn cases, include the minimum prior turns needed for interpretation and identify which turn contains the target answer.
- Add voice notes. Preserve representative WhatsApp audio, obtain the required permissions, and annotate language spans, transcription, entities, intent and expected action.
- Freeze evaluation splits. Keep development examples for prompt work and a locked test split for final comparisons. Prevent near-duplicate messages from leaking across splits.
- Record a version. Tag the dataset with a date, schema version, annotation guidance version and changes since the previous release.
Evaluate voice notes as two linked systems
Voice notes need their own modality slice. First evaluate the speech recogniser against a human transcript; then evaluate the assistant using the recogniser’s transcript. This separation reveals whether an incorrect answer came from speech recognition or from language and intent reasoning. Annotate code-switch boundaries, names, numbers, locations, hesitations and overlapping speech where they affect downstream decisions.
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ZAEBUC provides complementary written and spoken Arabic-English material, including Modern Standard Arabic, Gulf Arabic and Egyptian Arabic, and reports 930 code-switched spoken utterances. It can inform coverage decisions, but an external corpus should supplement—not replace—cases from the business’s own customers. Check its published usage terms before copying audio or text into a commercial evaluation system.
Score outcomes that matter to the business
| Dimension | Measure | What counts as a failure |
|---|---|---|
| Language handling | Correct span labels, Arabizi routing, language-consistent reply | Replies in an unexpected language or treats Arabizi as unrelated text |
| Intent | Per-intent precision, recall and confusion matrix | Routes a cancellation as a new order |
| Entities | Exact or policy-defined match for dates, numbers and locations | Changes “22” to “2” or loses an Arabic-Indic digit |
| Action and safety | Correct answer, refusal, clarification or handoff | Provides a confident answer where policy requires a person |
| Conversation state | Accuracy on multi-turn and short-context cases | Answers “yes” without resolving what it confirms |
| Speech path | Transcription quality plus downstream intent and entity scores | ASR drops a location and the assistant books the wrong branch |
| Regression | Change in each slice between builds | Overall score rises while Arabizi handoffs fall |
Use a confusion matrix and slice-level counts so reviewers can see which intents and language forms changed. Attach confidence intervals when sample sizes support them. Do not invent a pass threshold for the entire Gulf market; set thresholds by risk. A payment, identity or safety workflow should require stricter routing and escalation than a general opening-hours question.
Make uncertainty observable. Log the model’s language and intent confidence, the threshold applied, the selected action and whether a human accepted or corrected the result. When confidence is below the approved threshold, route to a person and preserve the full conversation context for the agent.
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Test handoff and bilingual conversation safety
A handoff test should verify the complete path: trigger detection, transfer to the correct queue, customer-facing explanation, transcript availability and suppression of further automated actions that could conflict with the agent. Include explicit requests for a human, policy-sensitive questions, repeated misunderstandings and cases where language detection is uncertain.
Check that the assistant follows the customer’s language for bilingual replies rather than switching unpredictably. If the customer changes language mid-conversation, the next response should follow the current policy while retaining prior intent and entity values. Define stop conditions for loops, abusive content, unresolved ambiguity and repeated low-confidence turns. The stop condition should be testable as a label and an expected event, not an informal instruction.
Run regression tests throughout the release cycle
Execute the locked set before launch and after prompt edits, model-version changes, retrieval or tool changes, and WhatsApp template changes. Compare the new run with the last approved run at the case level. A dashboard should show newly failed cases, fixed cases, unchanged failures and any slice whose count changed because the dataset itself was revised.
Keep failed examples in the repository with a short diagnosis: language detection, normalisation, intent boundary, entity parser, policy rule, speech recognition or handoff integration. Add a corrected or newly discovered case only after review, and never silently replace the original failure. Weekly scoring of anonymised bilingual conversations can reveal drift between the frozen test set and current customer language; promote recurring, approved patterns into the next version.
Deployment and governance requirements
- Use the WhatsApp Business Platform channel where customers already communicate, and document opt-in language and message-template rules.
- Record where messages, transcripts and annotations are processed and stored, who can access them, and when they are deleted.
- Define retention separately for raw audio, redacted text, model logs and human-agent transcripts.
- Document the handoff queue, service hours, fallback contact and what happens when no agent is available.
- Apply per-message language detection, treat Arabizi as Arabic for policy purposes, and preserve numerals exactly where downstream systems require them.
- Review bilingual samples for privacy leakage, unsafe advice and unintended disclosure before promoting a build.
External datasets can improve coverage, but licensing matters. Mixat documents a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license; that license may not fit a commercial evaluation pipeline without careful review. Keep third-party material clearly separated from customer-derived data and record attribution and permitted uses in the repository.
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Common failures and targeted fixes
The assistant answers in English to an Arabizi message
Check whether the language detector labels Arabizi as Arabic and whether the response policy consumes that label. Add paired Arabizi and Arabic-script cases, then verify the handoff path when detection confidence is low.
Intent is right but the location is unusable
Inspect the entity annotation and normalisation table. Add transliteration and landmark variants, preserve the raw span for audit, and test the exact value sent to the fulfilment system.
Voice-note tests look good end to end but customers still report errors
Run the recogniser-only evaluation. If the transcript drops numerals, names or dialect words, fix audio handling or transcription before changing the assistant prompt. If the transcript is accurate, investigate intent and policy scoring separately.
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Review routing and refusal slices independently. Weight high-risk cases by consequence, not volume, and block release when required handoffs are missed even if the overall average rises.
Short confirmations are interpreted inconsistently
Check that the test runner supplies the same preceding turns as production. Add ambiguous-context cases with clarification as the expected action, and ensure the system does not reuse stale state from an earlier conversation.
Quick Recap
Release checklist
- Several hundred anonymised, inbox-derived cases have stable IDs and documented sampling dates.
- Arabic script, English, Arabizi, Gulf dialect, code-switching, spelling variation and both digit systems have explicit slices.
- Dates, numbers, locations and policy-sensitive entities have expected values and preservation rules.
- Multi-turn context, ambiguous short replies and explicit human requests are represented.
- Voice notes have separate transcription and assistant annotations.
- Every case has an expected action, including
route_to_personwhere appropriate. - Scores are reported by language, intent, modality, risk and release version, with slice counts.
- Low-confidence thresholds, stop conditions and agent-context requirements are tested end to end.
- Opt-in, retention, processing location, access control and third-party license decisions are documented.
- The locked set runs before launch and after every prompt, model and template change.
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