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

• What user problem did you solve and for whom?
• What were the success criteria and constraints (e.g., latency SLOs, cost per request, compliance)?

2) Data Sourcing and Governance

• Sources and modalities (structured/unstructured, internal/external).
• Size and quality (volume, coverage, freshness, label quality, dedup/OCR issues).
• Privacy, PII handling, access control, consent, retention, residency.

3) Model Choice and Architecture

• Rationale for encoder–decoder, instruction-tuned LLM, RAG, or hybrid.
• Orchestration: query routing, tools, vector retrieval, rerankers, and any function calling.

4) Training and Fine-Tuning

• Objectives (SFT, preference optimization like DPO/KTO, contrastive for embeddings).
• Datasets, augmentation/synthetic data, and curriculum.
• Hyperparameters, scaling strategy, and infra.

5) Evaluation

• Offline metrics (retrieval quality, faithfulness, toxicity, hallucination rate).
• Human evaluation protocol and acceptance criteria.
• Online A/B or interleaving.

6) Safety and Guardrails

• Toxicity, jailbreak, prompt injection, PII leakage mitigation.
• Policy enforcement and red-teaming.

7) Latency, Throughput, and Cost

• End-to-end latency budget and p95 targets.
• Throughput and concurrency limits.
• Cost per request and major cost drivers.

8) Key Failure Modes

• Where it breaks (data, retrieval, reasoning, safety) and mitigations.

9) Trade-offs

• What you optimized for and what you deferred.

10) 10x Scale Plan

• How you would evolve the system for 10x traffic while meeting a 200 ms p95 latency SLO and a 20% cost reduction.