System Design for the AI Era
Design distributed systems that gracefully carry LLM, retrieval, and agentic workloads — and survive the interviews.
Duration
8 weeks · 6-8 hours/week
Level
Advanced
Delivery
Online
Status
Open for enrollment
Why this technology matters.
System design for the AI era is the discipline of architecting distributed systems that carry LLM, retrieval, and agentic workloads without collapsing under latency, cost, or failure cascades. It matters now because AI features impose new pressures — token latency, GPU costs, eval-gated deploys, and nondeterministic failures — on top of every classic distributed-systems problem.
It is used by backend and platform teams to design APIs, queues, caches, and retrieval layers that keep AI products fast, affordable, and resilient. It solves scale, fault tolerance, and cost control for AI workloads, but it does not solve bad product framing or missing evaluation — elegant architecture cannot rescue a system whose quality bar was never defined.
By the end you will be able to build a distributed design for an LLM-powered product with latency and cost budgets, a retrieval-backed service design with caching and fallback paths, and an interview-ready architecture walkthrough covering trade-offs and failure modes.
Why this course exists
The gap is between drawing boxes for a take-home exercise and designing systems that survive real AI traffic with its latency, cost, and eval constraints. The course teaches the arc from requirements to data and retrieval design to serving infrastructure to evaluation and operations, so students can reason about trade-offs the way production and interview rooms demand.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Strong backend or distributed systems experience
- Familiarity with APIs, queues, and databases
- Basic understanding of LLM applications
Technologies & tools
Skills you'll gain
A 8 weeks arc, module by module.
- Module 01
Week 1 — Decomposition and constraints
- Module 02
Week 2 — Storage, queues, and consistency
- Module 03
Week 3 — API and streaming contracts
- Module 04
Week 4 — Scaling patterns: sharding, caching, and backpressure
- Module 05
Week 5 — AI in the path: inference, retrieval, and agents
- Module 06
Week 6 — Cost, latency, and SLO design
- Module 07
Week 7 — Failure modes and operability
- Module 08
Week 8 — Mock reviews and design critiques
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Decompose requirements into scalable system architectures.
- Design for the AI-specific concerns: latency, cost, eval, and failure modes.
- Communicate trade-offs clearly in a system design interview.
- Produce a design document ready for an engineering review.
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Scalable inference serving design
- Project 02
Retrieval-backed assistant architecture
- Project 03
Agentic workflow system with failure handling
- Capstone
Interview-ready distributed AI system design with latency and cost analysis
Speak the language first.
- Distributed system fundamentals
- Core ideas like scaling, replication, and partitioning that let services handle large AI workloads reliably.
- Latency budgeting for LLM apps
- Splitting the total response-time allowance across retrieval, inference, and post-processing so each part stays fast.
- Retrieval-augmented serving paths
- Request flows that fetch relevant documents before generation so answers stay grounded and current.
- Agentic workload orchestration
- Coordinating multi-step tool calls and model steps with queues, retries, and state tracking.
- Caching for inference
- Reusing prior embeddings, retrieval results, or completions to cut cost and response time.
- Cost-aware capacity planning
- Estimating token, compute, and storage spend per request so scaling decisions stay within budget.
- Failure modes of AI systems
- Known breakdowns such as cascading retries, overloaded inference queues, and stale indexes, with planned mitigations.
- Observability for AI pipelines
- Logging prompts, retrieval hits, latencies, and quality signals so problems can be traced end to end.
- Interview-style trade-off analysis
- Comparing design options on latency, cost, accuracy, and operability to justify choices clearly.
Fix, check, and go deeper.
Troubleshooting & common mistakes
P99 latency spikes under load
Trace the request path to find the slow stage, add caching or concurrency limits there, and shed or queue excess agentic retries.
Inference costs explode with traffic
Measure tokens per request by stage, add caches and smaller-model fallbacks, and cap runaway retry and context sizes.
Stale index serves outdated answers
Check index freshness lag, shorten the re-index interval for hot sources, and serve a freshness flag until the update lands.
Cascading failures from retry storms
Add timeouts, backoff with jitter, and circuit breakers between services, then replay traffic gradually after recovery.
Inconsistent answers across replicas
Pin model, prompt, and index versions per deployment, verify replica configs match, and route sticky sessions during transitions.
Eval-blind deploys ship regressions
Gate releases on latency, cost, and quality checks, canary the new design, and roll back when any gate breaches.
Before you move on, you should be able to
- Explain how distributed components carry LLM and retrieval load
- Design serving paths that balance latency, cost, and accuracy
- Build caching and queueing strategies for agentic workloads
- Evaluate designs with explicit trade-off reasoning
- Diagnose latency, cost, and failure cascades in AI systems
- Deploy observable pipelines with versioned models and indexes
- Present a system design clearly under interview conditions
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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