Handling Edge Cases And Exceptions

Why this matters

As a Prompt Engineer, your prompts will face real users, messy data, and unpredictable requests. Edge cases and exceptions are where models often fail: ambiguous questions, conflicting instructions, missing context, outdated facts, rare terms, mixed languages, or unsafe topics. Designing for these scenarios improves reliability, safety, and user trust.

Who this is for

• Prompt Engineers building production-grade assistants or tools.
• Data Scientists evaluating model robustness across domains.
• Product folks defining behavior for failure and uncertainty.

Prerequisites

• Basic prompt patterns (system/content/format instructions).
• Awareness of model limitations (hallucinations, truncation, outdated knowledge).
• Basic evaluation mindset (test cases, acceptance criteria).

Concept explained simply

Edge cases are requests the model didn’t see often in training or that violate assumptions: vague inputs, missing units, mixed languages, novel entities, or conflicting constraints. Exceptions are safety, policy, or capability boundaries that require refusal, clarification, or fallback.

Mental model

Think like an air-traffic controller with checklists: detect anomalies, slow down, confirm, and route safely. Your prompt should guide the model to:

1. Detect uncertainty or risk.
2. Clarify missing info or choose a safe default.
3. Constrain output format and scope.
4. Verify and self-check before finalizing.
5. Escalate or politely refuse when needed.

Patterns and techniques

1) Detect → Clarify → Constrain → Verify → Escalate

System: You are a careful assistant. When uncertain, ask 1-2 clarifying questions before answering. If unsafe or out-of-scope, explain why and offer safe alternatives.

Assistant policy:
- Detect: If ambiguity/missing units/mixed language/conflict → flag.
- Clarify: Ask concise questions (max 2) or pick documented defaults.
- Constrain: Use provided schema; keep answers brief and cite assumptions.
- Verify: Self-check for math, dates, units, and contradictions.
- Escalate: If still unsure → provide best-effort + uncertainty note or refuse.

Output format:
- reasoning_summary: one short sentence
- action: clarify | answer | refuse
- final: the user-facing text

2) Safe defaults and graceful refusals

• When units/locale missing → ask or default to a clearly stated fallback.
• When unsafe or medical/legal advice → refuse and suggest safer alternatives.
• When knowledge may be outdated → state uncertainty and suggest verification path.

3) Self-check prompts

Before final answer, silently run checks:
- Are assumptions clearly labeled?
- Are numbers consistent (units, conversions)?
- Did I follow the requested language/format?
- Any contradiction or policy risk?
If any check fails → revise once.

4) Structured clarifications

5) Long or noisy inputs

• Ask user permission to summarize before answering.
• Process in chunks with a shared outline.
• Enforce a compact output schema to avoid drift.

Worked examples

Example 1: Ambiguous math with missing units

User: "Add 15% tax to 1200" (no currency, unknown region)

System: Compute totals precisely.
User: Add 15% tax to 1200.

System: Ask for missing critical info (currency/region) or state a clear default.
User: Add 15% tax to 1200.
Assistant (expected):
- reasoning_summary: Missing currency/region.
- action: clarify
- final: "Which currency and region should I use? If not specified, I will use USD and simple percentage, total = 1200 * 1.15. Proceed?"

Example 2: Potentially outdated knowledge

User: "What is the current prime rate in Canada?"

System: If asking for current values, warn about possible outdated knowledge.
Assistant:
- reasoning_summary: Time-sensitive request.
- action: answer
- final: "Rates change frequently and may be updated after my last knowledge. I can explain how to find the official current rate and typical factors influencing it. Would you like the process and context?"

Example 3: Mixed language

User: "¿Puedes resumir este artículo? I need it in English."

System: Detect language and follow the user's requested output language.
Assistant:
- reasoning_summary: Input Spanish, output English requested.
- action: clarify
- final: "I will summarize the Spanish text in English. Please paste the content (or say 'short' for a brief summary, 'detailed' for more depth)."

Example 4: Very long input

User pastes 10,000+ characters and asks: "Give me key risks."

System: If input is long, summarize first with an outline, then extract risks.
Assistant:
- reasoning_summary: Lengthy input, risk of truncation.
- action: clarify
- final: "The text is long. I will first produce a 5-bullet outline, then list top 5 risks with 1-sentence rationale each. OK?"

Exercises

These mirror the exercises below. The quick test is available to everyone; only logged-in users will see saved progress.

Exercise 1 — Clarify missing locale and units

Design a prompt that handles: "Give me the tax on 1200" with unknown region and currency. It should either ask 1–2 concise clarifying questions or apply a documented default and label the assumption. Constrain the output to a short JSON with keys: reasoning_summary, action, final.

Exercise 2 — Out-of-date info fallback

Create a two-step prompt that handles a time-sensitive query: "What is the unemployment rate this month?" The assistant should: (1) detect time sensitivity and either request a date/source or explain limitations; (2) provide a safe, useful response without pretending to have current data.

Checklist before shipping

• Clarifies missing critical info (units, locale, time, scope)
• Explicit safe defaults and refusals are defined
• Self-check step for math, dates, contradictions
• Handles long inputs (summarize → answer)
• Language detection and output language confirmed
• Output schema enforced (predictable format)
• Clear uncertainty notes for time-sensitive facts

Common mistakes and how to self-check

• Skipping clarifications: Add a hard rule to ask max 1–2 concise questions for critical gaps.
• Hidden assumptions: Force the model to label assumptions in the final answer.
• Overlong answers: Constrain outputs with max bullets or word limits.
• Ignoring language: Require explicit "input_language" and "output_language" fields.
• No uncertainty signal: Include an "uncertainty" note for time-sensitive queries.

Practical projects

• Edge-case harness: Build a set of 50 tricky prompts (ambiguous, long, multilingual, conflicting) and a rubric to score clarify/answer/refuse behavior.
• Safety-first assistant: A prompt that triages financial/medical/legal questions to either refuse or provide general, non-advisory info with clear disclaimers.
• Long-text analyzer: A prompt flow that summarizes first, confirms scope, then extracts key risks with a stable JSON schema.

Learning path

1. Collect domain edge cases (10–20 examples from real users).
2. Define safe defaults and refusal criteria per domain.
3. Create a standard clarify → constrain → verify template.
4. Design a self-check rubric and embed it in prompts.
5. Build a small evaluation set and measure pass rates weekly.
6. Iterate on failures; add new cases to your harness.

Mini challenge

Draft a single prompt that can handle both: (a) "Translate to French and summarize in 3 bullets" when given an English paragraph, and (b) "Summarize in English" when given a French paragraph. It must detect input language, confirm the requested output language, and refuse if the text includes sensitive personal data.

Next steps

• Turn your best pattern into a reusable template for your team.
• Expand your edge-case library monthly; keep measuring.
• Integrate uncertainty and safety signals into your product UI.