SambaNova Systems India Prompt Engineer Interview: Rounds, Questions and Preparation

What the job involves

Prompt Engineers in AI and deeptech design, test, and refine prompts that guide large language models and other generative systems to produce accurate, safe, and useful outputs for products such as code assistants, document analyzers, or domain-specific copilots. Day to day, they work with ML engineers and product teams to run prompt experiments, evaluate model responses, build reusable prompt templates, and document failure modes. At established deep tech employers, the role often sits close to model deployment, so prompt quality is tied directly to latency, cost, and customer trust.

Role and technical knowledge

1. 1

   What is the difference between zero-shot, few-shot, and chain-of-thought prompting, and when would you use each?

   A strong answer defines each pattern, gives a concrete use case, and notes that chain-of-thought can improve reasoning but may increase token cost and expose intermediate steps.

2. 2

   How would you evaluate whether one prompt is better than another for a summarization task?

   Cover both automatic metrics (ROUGE, BERTScore, exact match) and human review for factuality, coverage, and tone, plus a small held-out test set to avoid overfitting to a few examples.

3. 3

   Explain how tokenization and context window size affect prompt design and cost.

   Mention that text is split into tokens, longer prompts consume more of the context window and cost more, and that chunking or retrieval can keep prompts within limits.

4. 4

   What is retrieval-augmented generation, and why is it useful for enterprise AI products?

   Explain that RAG fetches relevant documents at inference time to ground the model, reducing hallucination and allowing private or up-to-date knowledge without retraining.

Practical scenarios

1. 5

   A customer support bot powered by an LLM starts giving confident but wrong answers about refund policies. How would you debug and fix the prompt?

   Suggest checking retrieval sources, adding explicit instructions to say 'I do not know' when unsure, using few-shot examples of correct refusals, and testing with adversarial queries.

2. 6

   You need to build a prompt that extracts structured JSON from unstructured invoices. What steps would you take?

   Describe defining a strict schema, giving one or two examples, validating output programmatically, handling missing fields, and iterating on edge cases like multi-currency or handwritten text.

3. 7

   How would you reduce the cost and latency of a prompt that currently sends a 10-page document to the model for every query?

   Propose chunking with retrieval, summarizing or compressing context, caching frequent answers, and measuring quality after each change to ensure no major regression.

4. 8

   Your prompt works well in English but fails for Hinglish or regional language inputs. What is your approach?

   Discuss testing with real user data, adding multilingual examples, using translation or language detection, and checking whether the base model supports the language well enough.

System design and collaboration

1. 9

   How would you version and document prompts so that a team of engineers can reuse them safely?

   Mention storing prompts in Git with clear names, linking each to evaluation results, documenting intended use and limitations, and using a registry or config system rather than hardcoding.

2. 10

   How do you decide when to fix a problem with prompt engineering versus when to fine-tune or change the model?

   A strong answer weighs data availability, cost, latency, and how often the failure occurs; prompt fixes are faster and cheaper, while fine-tuning suits repeated domain-specific behavior.

3. 11

   Describe how you would set up a human-in-the-loop review process for prompt outputs in a high-stakes domain like healthcare or finance.

   Cover sampling outputs, defining review rubrics, tracking disagreement, escalating unsafe responses, and feeding corrections back into prompt or retrieval improvements.

4. 12

   What safety and bias checks would you include before shipping a prompt to production?

   Mention red-teaming for harmful outputs, testing across demographics and languages, checking for PII leakage, and adding guardrails or refusal instructions where needed.

Behavioural and HR

1. 13

   Why do you want to work as a Prompt Engineer in a deep tech AI company rather than a general software role?

   Show genuine interest in model behavior, evaluation, and product impact, and connect it to specific projects or coursework you have done.

2. 14

   Tell me about a time you had to learn a new AI tool or technique quickly for a project.

   Use a STAR structure, focus on how you found reliable resources, built a small test, and applied the learning under a deadline.

3. 15

   How would you handle a situation where your prompt performs well in testing but a senior engineer says it is not production-ready?

   Demonstrate openness, ask for specific concerns like latency, safety, or edge cases, and propose a joint evaluation plan rather than defending your work.

4. 16

   Where do you see prompt engineering heading as models improve, and how will you stay relevant?

   Acknowledge that prompting may shift toward evaluation, data curation, and system design, and mention continuous learning through papers, open-source tools, and hands-on projects.