Question 1

How do you build a production-grade multi-agent AI system?

Accepted Answer

Building production-grade multi-agent systems requires a clear separation of agent roles, a reliable orchestration layer (such as LangGraph or Agno), and robust fallback and escalation logic. Each agent should have a narrow, well-defined scope. State management is critical — using a persistent store like Firestore or Redis ensures agents share context reliably across sessions. Observability tools like LangSmith or Arize AI should be wired in from day one to trace agent decisions and catch failure modes before they reach users.

Question 2

What is RAG and when should you use it?

Accepted Answer

RAG (Retrieval-Augmented Generation) combines a vector search index with an LLM to answer questions grounded in your own data. Use it when the LLM needs access to private, proprietary, or frequently updated information that was not part of its training data. For high-accuracy use cases such as compliance or legal documents, hybrid RAG — combining dense vector search with BM25 sparse search — significantly outperforms pure vector retrieval by capturing both semantic meaning and exact keyword matches.

Question 3

What are the biggest security risks in LLM-powered applications?

Accepted Answer

The most critical LLM security risks are prompt injection (malicious inputs that hijack the model's instructions), training data extraction (models leaking sensitive data they were trained on), jailbreaking (bypassing safety guardrails), and data poisoning in RAG pipelines (corrupting the retrieval index). Mitigations include input and output validation layers, strict system prompt design, sandboxed tool execution, and monitoring with guardrail frameworks. The OWASP Top 10 for LLMs and the NIST AI RMF are the leading frameworks for structuring these defenses.

Question 4

What does the EU AI Act require for AI systems?

Accepted Answer

The EU AI Act classifies AI systems by risk level. High-risk systems (used in healthcare, hiring, or critical infrastructure) require human oversight, audit trails, bias testing, and transparency documentation before deployment. General-purpose AI models like LLMs must disclose that content is AI-generated and publish summaries of training data. Organizations deploying AI in the EU need a governance framework covering risk assessments, incident reporting, and ongoing monitoring — aligned with the NIST AI RMF as a complementary standard.

Question 5

How do you scale an AI system to millions of users?

Accepted Answer

Scaling AI systems to millions of users requires stateless, horizontally scalable services deployed via containers on platforms like GCP Cloud Run or AWS ECS. Async processing queues handle traffic spikes without blocking. Real-time state (conversation context, session data) should live in a fast store like Firestore or Redis, while high-volume analytics flow through BigQuery. LLM API calls are the main cost and latency bottleneck — prompt caching, request batching, and smart routing to smaller models for simpler intents significantly reduce both.

Question 6

What is LLMOps and why does it matter?

Accepted Answer

LLMOps is the practice of operating LLM-powered systems in production reliably — covering model versioning, prompt versioning, evaluation pipelines, observability, and cost management. Unlike traditional MLOps, LLMOps must handle non-deterministic outputs, prompt drift, and context window constraints. Key tools include LangSmith and Arize AI for tracing and evals, and frameworks like LangGraph for orchestration. Without LLMOps, teams discover failures through user complaints rather than monitoring dashboards.

Question 7

How do you choose between AWS and GCP for an AI backend?

Accepted Answer

GCP has a natural edge for AI workloads due to its tight integration with Vertex AI, BigQuery ML, and Google's own LLM ecosystem (Gemini, Dialogflow CX, Google Conversational Agents). It is the stronger choice when you are building on Google's AI stack or need real-time analytics at scale via BigQuery. AWS is the better default for general backend infrastructure, has the widest service catalog, and is often preferred for HIPAA-compliant workloads via its Business Associate Agreement. For most AI-first products, GCP for the AI layer and AWS for the broader infrastructure is a common hybrid pattern.

Question 8

Who is Aun Raza?

Accepted Answer

Aun Raza is an AI Engineering Leader and Backend Architect with 8+ years of experience building production-grade AI systems, LLM-powered platforms, and scalable backends. He currently leads CX automation at TON Foundation, shipping AI systems that serve over 1 million active users on Telegram. He has worked across Web3, FinTech, HealthTech, HRTech, and PropTech, and is currently completing Stanford's AI Security course (XACS134).

BuildingProduction-GradeAI That Ships

Where I’ve Worked

CX Automation and AI Engineering Lead

Senior AI Developer Engineering Manager

Engineering Manager (Tech Lead)

Team Lead Senior Software Developer

Senior Python Developer

Software Engineer (Machine Learning Python Developer)

Software Engineer

Software Engineer

Intern

Tools & Technologies

AI & LLM Systems

Backend & Architecture

Leadership & Delivery

Selected Work

TON Foundation CX Automation Platform

Hivin

Sinecure

Privacy-First HealthTech

Flash (Paymob Unified Checkout)

Landlord (Manager and Tenant)

Featured Articles

Designing AI Automation for Millions: CX Lessons from the Front Lines

LangSmith vs. Phoenix by Arize AI: Choosing the Right Tool for LLM Observability

Protecting LLMs in Production: Guardrails for Data Security and Injection Resistance

The Evolution of AI Memory: From Context Windows to True Long-Term Memory

AI in Healthcare: How LLMs Are Transforming Medical Documentation and Decision Making

The Humans Who Will Thrive in an AI-First World

Kind Words

Let’s Build
Together

BuildingProduction-GradeAI That Ships

Where I’ve Worked

CX Automation and AI Engineering Lead

Senior AI Developer Engineering Manager

Engineering Manager (Tech Lead)

Team Lead Senior Software Developer

Senior Python Developer

Software Engineer (Machine Learning Python Developer)

Software Engineer

Software Engineer

Intern

Tools & Technologies

AI & LLM Systems

Backend & Architecture

Leadership & Delivery

Selected Work

TON Foundation CX Automation Platform

Hivin

Sinecure

Privacy-First HealthTech

Flash (Paymob Unified Checkout)

Landlord (Manager and Tenant)

Featured Articles

Designing AI Automation for Millions: CX Lessons from the Front Lines

LangSmith vs. Phoenix by Arize AI: Choosing the Right Tool for LLM Observability

Protecting LLMs in Production: Guardrails for Data Security and Injection Resistance

The Evolution of AI Memory: From Context Windows to True Long-Term Memory

AI in Healthcare: How LLMs Are Transforming Medical Documentation and Decision Making

The Humans Who Will Thrive in an AI-First World

Kind Words

Let’s BuildTogether

Let’s Build
Together