Applied · AI System Designsenior

Conversational Analytics (Text-to-SQL)

Components

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Utilities

Text Box

Group / Container

Circle

Diamond

Divider Line

Heading

Recommended

Client / User

LLM / Model

Retriever

Knowledge Source

Tool / Function

Cache (semantic/prompt)

Database / State

Guardrail / Classifier

Entry & gateway

Client / User

LLM Gateway

API Gateway

Orchestration

Orchestrator / Planner

Agent / Worker

Agent Loop

Models

LLM / Model

Model Router

Embedding Model

STT / TTS

Retrieval / RAG

Retriever

Reranker

Vector Store

Knowledge Source

Tools & execution

Tool / Function

MCP Server

Code Sandbox

Browser / Computer Env

External API

Memory & state

Working Memory

Long-Term Memory

Cache (semantic/prompt)

Safety & eval

Guardrail / Classifier

PII Filter / Redactor

Eval / Judge

Observability / Tracing

Infra & data

Database / State

Queue / Event Bus

Human

Human-in-the-Loop

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connect · iterate · submit for review

// brief

Conversational Analytics (Text-to-SQL)

Let non-technical users ask questions in plain English and get correct answers from a data warehouse with thousands of tables.

Key Requirements

01Schema linking/retrieval at scale (retrieve relevant tables, not all)
02SQL validation: dry-run, cost estimate, column existence
03Read-only execution with row limits and cost caps
04Row/column-level permissions enforced
05Self-correction loop on SQL errors; show the SQL for trust

Rubric must-haves

0/3

Retrieves relevant schema before generating SQL
Has a validation/execution tool (dry-run, cost cap)
Has a safety/cost guardrail

These checks are the structure score, 30% of your total. They update as you draw.

Practise it like the real round

45-minute timer paced through requirements, design and deep dive. The follow-up questions stay hidden until the last phase, and solution hints are off.

AI Review

0/5

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