What should you put in your first prompt?

Give the task, audience, source facts and requested output. A prompt is the instruction you send to the model. OpenAI describes prompt engineering as writing instructions that help produce the required result in its prompt engineering guide.

Specify the reader and writing system

Azerbaijani is spoken primarily in Azerbaijan and northwestern Iran, with different writing systems used across communities. The language overview describes those differences. For a business serving customers in Azerbaijan, explicitly request Azerbaijani in Latin script.

Use this starter: “Write in Azerbaijani using Latin script for customers in Azerbaijan. Use short, courteous sentences. Keep product names unchanged.” Then add what you want written and the facts it should contain.

Separate instructions from information

• Task: Draft a reply to a delivery question.
• Reader: A customer placing their first order.
• Facts: Paste your approved delivery policy.
• Output: One short paragraph and one necessary question.
• Boundary: Ask about missing details instead of inventing them.

You do not need a grand role such as “world expert.” Tell the assistant what the customer needs to understand. If you have an approved Azerbaijani message, include it as a style example and label it separately from the facts.

For your first attempt, request one draft. Read it before asking for alternatives. Choose the version that communicates the offer clearly, and save the brief alongside the approved wording for later use.

Does Azerbaijani language support mean equal accuracy?

Do not assume equal performance across languages or tasks. The GPT-4 technical report describes multilingual evaluation, but that historical assessment does not establish the accuracy of every current ChatGPT response in Azerbaijani. Read its scope in the original report.

For business use, judge the actual output against your information. Separate natural language from correct information. A smooth sentence should still earn its place by answering the customer accurately.

If nobody on your team can review Azerbaijani, arrange language review before publishing important customer copy. For an internal experiment, mark the draft as unreviewed. Treat approval as a separate step with a named person responsible for it.

When evaluating existing social content, add these checks to your social media audit. Review the message itself alongside its presentation.

Which business tasks make useful first experiments?

Choose a task where you already know the right facts. The examples below are hypothetical exercises, not reports of measured customer results. Replace the placeholders with your own approved information.

Hypothetical example: a florist answering an enquiry

Draft an Azerbaijani reply in Latin script. The customer asks whether this bouquet can be delivered to their address. Approved information: [delivery areas and conditions]. Ask for any missing detail. Do not confirm availability or timing unless the information explicitly supports it.

Before sending, compare the delivery promise with your actual arrangements. If the customer has not supplied an address, keep the question that asks for it. Connect the message to your wider florist marketing plan so the offer and ordering instructions agree.

Hypothetical example: a bakery writing product copy

Write a short Azerbaijani product description from these facts: [ingredients, size and ordering details]. Use plain wording for someone choosing a gift. Preserve the ingredient list exactly. Do not add dietary, health or availability claims.

Read the description beside the original product record. Reject any unsupported promise, even if it sounds attractive. For the next customer step, use the ordering route you have chosen in your bakery marketing plan.

Hypothetical example: a freelancer preparing an appointment message

Ask for a short message confirming only the date, time, location and service you supply. Request a separate list of missing information. Check the final message against the appointment record before using it with your appointment booking process.

Keep these experiments as drafts during the first trial. Do not connect an unreviewed example directly to customer messaging. Decide who approves each message and where the approved wording will be stored.

How do you fix an answer that sounds wrong?

Name the problem before requesting another draft. Decide whether the issue is meaning, tone, missing information or an unsupported claim. Give one clear correction that preserves the parts already approved.

For awkward wording

Rewrite this in natural Azerbaijani for a customer message. Keep the meaning, names and numbers unchanged. Follow the tone of this approved example: [example]. Show any wording whose meaning you are uncertain about.

Avoid asking only for a “more human” version. Instead, identify what you want changed: shorter sentences, a warmer greeting or a clearer request. Read the revision aloud with your reviewer and keep the version that sounds appropriate for the business.

For invented information

The GPT-4 report acknowledges hallucinations, meaning plausible but incorrect output. That limitation is documented in the technical report. Check unsupported claims against your records instead of merely asking for more confident wording.

Compare this draft with the supplied notes. Remove claims that the notes do not support. List missing information as questions for me. Keep confirmed product details unchanged.

After that pass, verify the answer yourself. Do not treat the assistant checking its own work as independent confirmation. Open any cited page and make sure it supports the relevant statement.

Save useful corrections in a short style sheet. Include preferred greetings, product names and examples of approved wording. Remove customer identifiers from reusable examples when those details are unnecessary for the writing task.

Why are separate models built for lower-resource languages?

Think of the aim as concentrating development and testing on a particular language. Here, “lower-resource” refers to limited training and evaluation material for a language task. It is not a judgment about the richness of the language.

One concrete project, gpt-wiki-az, describes training on Azerbaijani Wikipedia text in its model card. That description establishes its stated training source. It does not prove superior customer service writing or readiness for your business.

When assessing AI tools for Azerbaijani language work, request examples from your own tasks. Ask what the model was tested on and who reviewed the language. Keep the same facts and acceptance criteria when comparing outputs.

Use ChatGPT as your initial trial if that is the tool you are evaluating. Consider another model when you can name the unresolved need. Avoid making a switch solely because a project has Azerbaijani in its description.

How should you run a useful trial this week?

Pick one recurring message and compare the full drafting process. Include your review and correction time. Decide beforehand what would make the workflow worth keeping.

1. Choose a few representative requests, including one with missing information.
2. Prepare approved facts and a sample of acceptable wording.
3. Use the same brief structure for each request.
4. Record factual corrections and language edits separately.
5. Revise the brief around the most frequent problem.

Keep the output under review until you understand the exceptions. If you later connect approved templates to Instagram, use the automatic Instagram reply guide to plan the messaging workflow.

End the trial with a concrete decision: keep the prompt, improve it or stop using it for that task. Retain the examples behind your decision so another team member can understand it.