Design an enterprise RAG agent system
Design an Enterprise RAG Agent System
Design an enterprise-grade AI assistant intended for internal company knowledge retrieval and task execution. The system's primary function is to answer employee questions by leveraging a diverse set of internal documents, including policies, product manuals, support tickets, reports, PDFs, spreadsheets, and knowledge-base articles.
The architecture should initially be based on Retrieval-Augmented Generation (RAG) principles. However, it must also incorporate agentic capabilities to handle tasks requiring multi-step reasoning or the use of external tools.
Your design discussion should cover the following key areas:
Core Concepts
- Differentiate between a base Large Language Model (LLM), a RAG application, and an AI agent.
- Explain how agents acquire and utilize capabilities through tools, APIs, planners, and execution policies.
System Design
- Propose a memory architecture for the system, encompassing both short-term conversational state and longer-term user or task-specific memory.
- Detail strategies for supporting many concurrent users while ensuring session isolation and overall system reliability.
- Describe how to build long-running or multi-agent workflows that can pause, retry, recover from failures, and maintain durability.
Evaluation and Security
- Outline methods for evaluating critical aspects such as answer quality, grounding, overall task success, system latency, and operational cost.
- Discuss defense mechanisms against common vulnerabilities like prompt injection, unsafe tool execution, and potential data leakage.
Data Handling
- Identify various document types typically found in a large enterprise environment.
- Analyze the specific technical challenges these document types present for ingestion, indexing, and retrieval processes.
AI interview prep
Core dilemma
Balancing the power and flexibility of AI agents (multi-step reasoning, tool use) with the critical enterprise requirements of reliability, security, auditability, and predictable performance, especially when dealing with diverse, sensitive internal data.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design an enterprise RAG agent system
Design an Enterprise RAG Agent System
Design an enterprise-grade AI assistant intended for internal company knowledge retrieval and task execution. The system's primary function is to answer employee questions by leveraging a diverse set of internal documents, including policies, product manuals, support tickets, reports, PDFs, spreadsheets, and knowledge-base articles.
The architecture should initially be based on Retrieval-Augmented Generation (RAG) principles. However, it must also incorporate agentic capabilities to handle tasks requiring multi-step reasoning or the use of external tools.
Your design discussion should cover the following key areas:
Core Concepts
- Differentiate between a base Large Language Model (LLM), a RAG application, and an AI agent.
- Explain how agents acquire and utilize capabilities through tools, APIs, planners, and execution policies.
System Design
- Propose a memory architecture for the system, encompassing both short-term conversational state and longer-term user or task-specific memory.
- Detail strategies for supporting many concurrent users while ensuring session isolation and overall system reliability.
- Describe how to build long-running or multi-agent workflows that can pause, retry, recover from failures, and maintain durability.
Evaluation and Security
- Outline methods for evaluating critical aspects such as answer quality, grounding, overall task success, system latency, and operational cost.
- Discuss defense mechanisms against common vulnerabilities like prompt injection, unsafe tool execution, and potential data leakage.
Data Handling
- Identify various document types typically found in a large enterprise environment.
- Analyze the specific technical challenges these document types present for ingestion, indexing, and retrieval processes.