Explain prompt engineering strategies for chatbots

Quick Overview

This question evaluates a candidate's competence in prompt engineering for conversational AI, covering message roles, few-shot exemplars, function/tool routing, structured output enforcement, decoding parameter tuning, hallucination reduction, and policy-driven refusal handling.

Explain prompt engineering strategies for chatbots

Company: EliseAI

Role: Software Engineer

Category: Machine Learning

Difficulty: medium

Interview Round: Technical Screen

Explain and demonstrate prompt engineering techniques to make the chatbot reliable, steerable, and safe. Discuss system vs. user messages, few-shot exemplars, function/tool calling, output formatting (e.g., JSON schemas), temperature/top-p tuning, and strategies to reduce hallucinations and handle refusals. Provide concrete prompts and an iteration plan you would follow under a strict two-hour time limit.

Overview: This question evaluates a candidate's competence in prompt engineering for conversational AI, covering message roles, few-shot exemplars, function/tool routing, structured output enforcement, decoding parameter tuning, hallucination reduction, and policy-driven refusal handling.

|Home/Machine Learning/EliseAI
EliseAI logo
EliseAI
Sep 6, 2025
mediumSoftware EngineerTechnical ScreenMachine Learning
20
0

Prompt Engineering for Reliable, Steerable, and Safe Chatbots

Context

You are designing a production-grade chatbot that must be reliable (consistent, correct, verifiable), steerable (follows task, tone, and policy), and safe (respects constraints, avoids harmful outputs). You have a strict two-hour time limit to propose and demonstrate prompt engineering techniques.

Requirements

Discuss and demonstrate the following with concrete prompts and an iteration plan:

  1. Message roles: system vs. user (and developer) messages and how to layer them.
  2. Few-shot exemplars: when and how to use them; good and bad examples.
  3. Function/tool calling: how to route tasks to tools and gate model outputs.
  4. Output formatting: enforce structured responses (e.g., JSON schemas) and parsing guardrails.
  5. Decoding parameters: temperature and top-p tuning with intuition.
  6. Reducing hallucinations: strategies and prompts; handling uncertainty.
  7. Handling refusals: policy-driven refusal style and alternatives.
  8. Provide ready-to-run prompts and a pragmatic iteration plan for a two-hour timebox.
Loading comments...

Browse More Questions

More Machine Learning•More EliseAI•More Software Engineer•EliseAI Software Engineer•EliseAI Machine Learning•Software Engineer Machine Learning