Prompt Engineering - lesson 4 | HackIndia Skills

Key point

The one rule

Do not ask for “a neat answer” when your code needs data. Ask for a strict shape, then validate that shape before using it.

Structured output turns a prompt from a chat into an API contract. Your app becomes easier to test.

Why structure helps

Hackathon AI features often fail because the model returns a paragraph when your UI expects a tag, score, or list. JSON is usually the simplest format for app output.

Use structure when you need to store results, filter them, show cards, trigger actions, or compare test cases.

Your to-do

Design the output

Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.

Name each field your app needs.
Choose a type for each field: string, number, boolean, array, or object.
Limit choices with allowed values where possible.
Say what to do when data is missing or uncertain.
Tell the model to return only the structured output.

0 of 5 done

Example

Prompt pattern

Here is a prompt for a student project that classifies hostel maintenance complaints. The app can show the category, urgency, and a short message to the warden.

You are helping a college hostel maintenance desk.

Task: Read the student's complaint and return JSON only.

Return this exact JSON shape:
{
  "category": "electricity | plumbing | cleaning | internet | furniture | other",
  "urgency": "low | medium | high",
  "summary": "string, max 20 words",
  "needsHumanReview": true
}

Rules:
- Use only one category.
- Set urgency to high only if safety, water leakage, no electricity, or blocked toilet is mentioned.
- If the complaint is unclear, use category "other" and set needsHumanReview to true.
- Return valid JSON only. No markdown. No extra text.

Complaint:
"Fan in room B-214 is sparking and smells burnt."

Example

Parse and validate

Even a good prompt is not enough. Your code must reject bad output and handle it safely.

This small JavaScript check is fine for a demo. For a larger app, use a validation library such as Zod.

const allowedCategories = ["electricity", "plumbing", "cleaning", "internet", "furniture", "other"];
const allowedUrgency = ["low", "medium", "high"];

function parseComplaintResult(modelText) {
  let data;
  try {
    data = JSON.parse(modelText);
  } catch {
    return { ok: false, error: "Model did not return valid JSON" };
  }

  if (!allowedCategories.includes(data.category)) return { ok: false, error: "Bad category" };
  if (!allowedUrgency.includes(data.urgency)) return { ok: false, error: "Bad urgency" };
  if (typeof data.summary !== "string" || data.summary.length > 140) return { ok: false, error: "Bad summary" };
  if (typeof data.needsHumanReview !== "boolean") return { ok: false, error: "Bad review flag" };

  return { ok: true, data };
}

Common mistake

Do not ask for JSON and then accept whatever comes back. Models can add notes, miss fields, or invent new labels.

Also avoid vague fields like “priority”: “urgent”. Use allowed values your UI already understands.

Tip

Make failure visible

In your demo, show what happens when validation fails. A simple fallback message is better than a broken screen.

For example: “Could not classify this complaint. Sending to manual review.” This proves you thought beyond the happy path.