Amazon·Software Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

Amazon SWE interview with a deep-dive question on generative AI work. Just the one question but it had a lot of surface area to cover.

Questions Asked (1)

Walk me through a generative AI or LLM project you worked on. What was the problem, what design and modeling decisions did you make, and what trade-offs around cost, latency, hallucination, and evaluation did you run into? What was the measurable impact?

Technical Trade-offsSystem DesignProduct Analytics & Metrics

Author's notes

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AI HintsAI Generated

Suggested Approach

Choose a specific LLM project where you owned key decisions, and structure your answer using a clear narrative: problem, design choices, trade-offs, and measurable impact. Emphasize the trade-offs you navigated (cost, latency, hallucination, evaluation) and quantify results with metrics that matter to Amazon (e.g., customer impact, efficiency gains).

Pro tip: Quantify trade-offs with real numbers (e.g., 'reduced latency by 40% at 10% higher cost') and tie them to business outcomes; Amazon values data-driven decisions and customer obsession.

1. Set the Context and Problem

Briefly describe the project, the business problem, and why generative AI/LLM was the right solution. Mention the users and the scale.

2. Explain Design and Modeling Decisions

Walk through key technical choices: model selection (e.g., fine-tuning vs. prompt engineering), architecture (e.g., RAG, agents), and any custom components. Justify why you chose them.

3. Discuss Trade-offs

Detail the trade-offs you encountered around cost, latency, hallucination, and evaluation. Explain how you balanced them and what decisions you made.

4. Highlight Evaluation and Iteration

Describe how you evaluated the model (offline metrics, human eval, A/B tests) and iterated to improve performance and mitigate hallucinations.

5. Quantify Impact and Learnings

Share measurable outcomes (e.g., cost savings, latency reduction, accuracy improvement, user engagement) and key learnings that could apply to future projects.

Key Points to Mention

Model selection rationale (e.g., GPT-4 vs. open-source, fine-tuning vs. prompting)
Architecture choices (e.g., RAG, vector databases, caching, fallback mechanisms)
Cost optimization techniques (e.g., token limits, model distillation, batch inference)
Latency reduction strategies (e.g., streaming, caching, parallel calls)
Hallucination mitigation (e.g., grounding, guardrails, human-in-the-loop)
Evaluation metrics and methods (e.g., BLEU, ROUGE, human eval, A/B testing, business KPIs)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.