Prompt Engineering - lesson 7 | HackIndia Skills

Key point

The core idea

Quiet failure is when the model returns a clean-looking answer that should not be trusted. No crash. No error. Just a wrong result with good grammar.

Your job is to catch these cases before the user acts on them.

What it looks like

Quiet failures are common in student projects because demos often test only happy paths. A model may invent a deadline, skip a required field, ignore a budget, or sound sure without proof.

For an internship-ready project, show that you checked more than whether the API returned text.

Common mistake

Do not trust polish

A well-written answer can still be bad. Fluency is not accuracy.

Do not mark a prompt as working just because it sounds natural in English or Hindi. Compare it with the user input and your expected rules.

Your to-do

Check four signals

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.

Check completeness: are all required fields present.
Check grounding: does each claim come from the user input or your data.
Check constraints: did it follow budget, dates, language, length, and format.
Check uncertainty: does it admit when the input is missing or unclear.

0 of 4 done

Example

Simple failure check

Here is a small JavaScript check for a project that suggests college event plans. It catches answers that look valid but miss important constraints.

Use checks like this after the model response, before showing it as final.

function checkEventPlan(input, output) {
  const failures = [];

  if (!output.title || !output.schedule || !output.budgetNotes) {
    failures.push("missing_required_field");
  }

  if (input.budgetRupees && output.estimatedCostRupees > input.budgetRupees) {
    failures.push("over_budget");
  }

  if (input.collegeHours && !output.schedule.includes(input.collegeHours)) {
    failures.push("ignored_college_hours");
  }

  if (output.confidence === "high" && output.sources?.length === 0) {
    failures.push("high_confidence_no_sources");
  }

  return {
    ok: failures.length === 0,
    failures
  };
}

const input = {
  budgetRupees: 5000,
  collegeHours: "after 4 PM"
};

const output = {
  title: "AI Workshop",
  schedule: "10 AM to 1 PM",
  budgetNotes: "Can be done with sponsor support",
  estimatedCostRupees: 8000,
  confidence: "high",
  sources: []
};

console.log(checkEventPlan(input, output));
// { ok: false, failures: ["over_budget", "ignored_college_hours", "high_confidence_no_sources"] }

Tip

Ask AI to help

You can ask ChatGPT, Gemini, or Claude to review your prompt outputs. Paste the input, your prompt, and the model answer. Ask: “List quiet failures. Check missing fields, unsupported claims, ignored constraints, and unsafe advice.”

Still verify the review yourself. AI reviewers can also miss issues.

Log bad cases

Keep a small failure log in your repo. This helps judges and recruiters see that you tested real cases, not only one perfect demo.

Include the input, the bad output, the failure type, and what you changed.

case_id,input_summary,failure_type,fix
1,"User asked for ₹300 meal plan",over_budget,"Added budget check and retry prompt"
2,"User gave no deadline",invented_deadline,"Prompt now asks model to say deadline missing"
3,"User wanted Hindi answer",ignored_language,"Added language field validation"