GenAI System Deep-Dive: End-to-End Design and Scale Strategy
Provide a structured walkthrough of a production-grade GenAI system you built end-to-end. Cover the following areas:
1) Problem Definition
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What user problem did you solve and for whom?
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What were the success criteria and constraints (e.g., latency SLOs, cost per request, compliance)?
2) Data Sourcing and Governance
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Sources and modalities (structured/unstructured, internal/external).
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Size and quality (volume, coverage, freshness, label quality, dedup/OCR issues).
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Privacy, PII handling, access control, consent, retention, residency.
3) Model Choice and Architecture
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Rationale for encoder–decoder, instruction-tuned LLM, RAG, or hybrid.
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Orchestration: query routing, tools, vector retrieval, rerankers, and any function calling.
4) Training and Fine-Tuning
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Objectives (SFT, preference optimization like DPO/KTO, contrastive for embeddings).
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Datasets, augmentation/synthetic data, and curriculum.
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Hyperparameters, scaling strategy, and infra.
5) Evaluation
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Offline metrics (retrieval quality, faithfulness, toxicity, hallucination rate).
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Human evaluation protocol and acceptance criteria.
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Online A/B or interleaving.
6) Safety and Guardrails
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Toxicity, jailbreak, prompt injection, PII leakage mitigation.
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Policy enforcement and red-teaming.
7) Latency, Throughput, and Cost
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End-to-end latency budget and p95 targets.
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Throughput and concurrency limits.
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Cost per request and major cost drivers.
8) Key Failure Modes
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Where it breaks (data, retrieval, reasoning, safety) and mitigations.
9) Trade-offs
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What you optimized for and what you deferred.
10) 10x Scale Plan
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How you would evolve the system for 10x traffic while meeting a 200 ms p95 latency SLO and a 20% cost reduction.