Tools / Prompt Engineering Interview questions

Answer: Zero-shot asks the model to perform a task with no examples. Few-shot provides a few input-output pairs to establish a pattern or style. Chain-of-Thought (CoT) explicitly asks the model to "think step-by-step," breaking down complex logic into intermediate reasoning steps to improve accuracy in math or symbolic tasks.

Answer: Temperature scales the probability distribution of the next token; low values (e.g., 0.1) make the model deterministic, while high values (e.g., 0.8) make it creative/random. Top-P (Nucleus Sampling) limits the token choice to a subset whose cumulative probability reaches P, ensuring the model ignores "long-tail" nonsensical words while maintaining diversity.

Answer: I use grounding via Retrieval-Augmented Generation (RAG) to provide factual context. In the prompt, I include a "null constraint" (e.g., "If you don't know the answer based on the context, say you don't know") and request citations to force the model to map its claims to the provided text.

Answer: Research shows that LLMs are best at retrieving information at the very beginning or very end of a prompt. When the context window is large, key information placed in the middle is often ignored or "forgotten." I mitigate this by placing the most critical instructions or data at the bottom of the prompt, closest to the output trigger.

Answer: I treat prompts like code. I use a Golden Dataset (benchmark) to run regression tests whenever a prompt changes. I track versions in Git or a prompt management tool, using A/B testing to compare the performance (accuracy/latency) of a new version against the production baseline before deployment.

Answer: I adapt the formatting to match the specific model's chat template (e.g., using Llama-3's specific header tags). Smaller or open-source models often need more few-shot examples and clearer structural delimiters (like XML or Markdown headers) to follow instructions as reliably as GPT-4.

Answer: I provide a strict JSON schema and a few-shot example of the expected output. I use System Messages to reinforce that no conversational "preamble" is allowed. Finally, I implement programmatic validation (like a try/except JSON parser) to catch and retry if the model fails the format.

Answer: Use RAG for factual accuracy, reducing hallucinations, and accessing dynamic or proprietary data. Use Fine-Tuning when you need to teach a specific tone, a niche vocabulary, or a complex structural format that is too "expensive" (in terms of tokens) to explain in a standard prompt.

Answer: I use prompt compression by removing "fluff" words and using concise shorthand. I also offload sub-tasks: instead of one massive prompt, I use a cheap, small model (like GPT-4o-mini) for classification and only use the expensive model for the final reasoning or synthesis.

Answer: I look at Accuracy/F1 Score against the Golden Dataset, Latency (Time to First Token), and Cost per 1k tokens. For qualitative tasks, I use LLM-as-a-Judge (using a superior model to grade the prompt output based on a rubric).

A: This occurs when a model processes untrusted third-party data (like a website or email) that contains hidden instructions designed to hijack the model. Prevention involves using clear delimiters (e.g., XML tags like <user_input>), treating all external data as low-privilege, and implementing output sanitization to check for malicious commands before they are executed.

A: A layered defense can be used: 1) Strengthening the System Message to prioritize safety over user instructions. 2) Using a moderation API to pre-filter inputs. 3) Instructional anchoring, where the core safety constraints are repeated at the very end of the prompt to leverage the "Recency Bias" of the model.

A: A pre-processing layer can be implemented that uses Regex or a dedicated NER (Named Entity Recognition) model to redact sensitive data (names, SSNs, emails) before the text ever reaches the LLM provider's API.

A: Focus on Expectation Management. If a stakeholder wants 100% factual accuracy on niche data without a RAG pipeline, explain the probabilistic nature of LLMs and propose a "Human-in-the-loop" (HITL) workflow or a fallback mechanism to maintain trust.

A: Follow ArXiv for new prompting papers (like "Chain-of-Density" or "Tree-of-Thoughts"), participate in developer communities (Discord/X), and maintain a personal sandbox where new model releases (e.g., Llama, Claude, Gemini) can be benchmarked against an existing prompt library.

A: Advocate for standardized documentation. Every prompt should include its "Intent," "Known Failure Modes," and the "Version of the Model" it was optimized for, ensuring that a teammate doesn't break a specific behavior by making a "minor" phrasing tweak.