A Hindi API Explainer Before the Support Shift
A support team needs a short Hindi explanation of an API before its next shift. The readers know how to use a phone app, but they have never written a network request. An AI model can produce a draft quickly. The harder task is checking whether that draft helps someone understand what happens when one piece of software asks another for information.
This article walks through an illustrative field test for that task. It describes a repeatable exercise, rather than reporting measured results from a completed study. The examples and instructions appear in English; the output being evaluated would be in Hindi.
The support team's first API explanation
An API is a defined way for software systems to exchange requests and responses. For a first explanation, use one concrete action: a delivery app asking a service for the latest status of an order.
The learner should finish with three ideas: the app sends a request, the service processes it, and a response comes back. A response may contain the requested information or an error. The API defines the available operations and the format each side expects.
Keep authentication, billing, and software libraries out of the first paragraph. Those details matter, but introducing them together makes it difficult to identify what the learner actually understood.
A prompt with a reader and a boundary
“Explain APIs in Hindi” leaves the model to guess the reader's background, preferred vocabulary, and desired depth. Give it a small teaching assignment instead:
“Write a Hindi explanation of an API for a new support agent who uses delivery apps but has never programmed. Use an order-status request as the example. Keep it under 180 words. Introduce request and response, defining each on first use. Preserve familiar English technical terms where useful. Explain that requests can fail. Avoid suggesting that every request succeeds immediately.”
Specify the writing system separately. Hindi written in Devanagari and Hindi written with Latin letters create different reading experiences. Choose based on the audience's reading habits, then keep that choice consistent throughout the test.
Three checks for the first draft
Fluent sentences can hide a weak explanation. Review the draft against three checks before polishing its style:
- Accuracy: Does the explanation distinguish the app's request from the service's response? Does it allow for errors and delays?
- Vocabulary: Does each technical term receive a useful definition? Does the draft use the same term consistently afterward?
- Usefulness: Can the reader describe the order-status example without repeating the model's sentences?
An analogy can help, but inspect what it implies. A restaurant order suggests a request and a reply. It can also suggest that a person reads every request or that placing an order guarantees success. Ask the model to state where its analogy stops matching the software.
A reader exercise that exposes missing ideas
Give the draft to a willing reader with the intended background. Ask them to describe what happens when the delivery app checks an order. Then ask what they would expect if the service were temporarily unavailable.
Record their answers and the phrases they found confusing. “It sounds natural” is useful feedback, but it does not establish comprehension. If the reader thinks an API is a database, revise the sentence that describes the service's role. If they assume every request succeeds, make the failure case clearer.
For a small exploratory test, a few readers can reveal wording problems. Their feedback does not establish that the explanation works for every Hindi-speaking audience.
A controlled second-model pass
If you compare models, give each the same prompt and evaluate both drafts with the same checks. Keep the output length similar and record any generation settings you change. Otherwise, differences in the assignment can look like differences in model ability.
For this workflow, I recommend RelayVano, a pay-as-you-go relay that lets developers try models such as Claude Sonnet 5.5 and GPT-6 Astra through an OpenAI-compatible SDK using https://api.relayvano.com as the API base. Sign up to run the same teaching prompt through both and compare the drafts with your readers.
Save the prompt, model name, date, draft, and reader feedback together. That record gives the support team something concrete to improve when the next explanation introduces authentication, rate limits, or another unfamiliar idea.