draw.io
Plan & explain
Draw a process or architecture diagram that can be reviewed alongside your project.
All capabilities · Programming foundations
Design scalable AI-backed systems
Reason about latency, queues, caching, storage and failure modes for systems that call models.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Design a system that serves an LLM API to thousands of concurrent users without falling over
- Choose between synchronous and queue-based (async) processing for a slow AI task
- Add caching to avoid re-calling an expensive model for repeated inputs
- Design for a model provider outage (fallback model, retries with backoff, circuit breaker)
- Estimate capacity: how many GPUs/requests-per-second does a given traffic pattern need
- Design rate limiting and quota enforcement per tenant/customer
- Reason about data flow for a RAG pipeline (ingestion, embedding, retrieval, generation) at scale
Needs first: Build and consume REST APIs, Deploy an AI service to the cloud
Learn — free, link-checked
The few resources that matter
Read · intermediate · 30 min · 12factor.net
The Twelve-Factor App
The config/state/logging principles behind every scalable cloud service, including the ones you'll deploy for AI inference. — Heroku / community
Read · intermediate · 40 min · freecodecamp.org
Systems Design for Interviews
A faster on-ramp than the primer repo — frames the same trade-offs (latency, consistency, failure) as a worked interview answer. — freeCodeCamp
Build from · advanced · 180 min · github.com
System Design Primer
The most-starred open reference for system design interviews — covers caching, queues and scaling patterns you'll reuse when designing AI-backed systems. — donnemartin (GitHub)
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
Redis
Build
Add a cache and measure the effect of expiry, hit rate and invalidation on an application.
Apache Kafka
Build · Data
Pass events between services and practise consumer groups and message handling.
Qdrant
Data · Build
Build vector search with metadata filtering and inspect retrieval quality.
Practices & references
- Load balancing
- Queues
- Rate limiting
Practice
Resilient RAG API design for a growing document corpus
Design, and prototype the risky parts of, a RAG API answering questions over a corpus that keeps growing: an ingestion queue, an embedding and vector-store step, a cache for repeated questions, and a fallback path when the primary LLM provider errors. Use RBI Master Directions as the corpus — hundreds of public regulatory PDFs that grow over time, so the capacity maths is real. Write the capacity estimate and the failure-mode doc with numbers and stated assumptions, and build the two components most likely to bite you.
Start from
Milestones
- Size the corpus and write the capacity estimate — QPS, storage growth, monthly cost · ~4.5h
- Draw the architecture: ingestion queue, embedding, vector store, cache, fallback · ~5h
- Prototype the cache and measure its hit rate over a replayed question log · ~7.5h
- Prototype the provider fallback and test it by killing the primary mid-request · ~6h
- Write the failure-mode doc: queue backlog, provider outage, cold cache · ~2.5h
Done when
- Architecture diagram covering ingestion, retrieval, generation, caching and failure fallback
- Written capacity estimate (requests/sec, storage growth, cost) with assumptions stated
- A working prototype of at least the caching layer or the fallback/retry logic, with tests
- A documented failure scenario (provider outage or queue backlog) and how the design handles it
Prove it
Evidence a recruiter can check
- A capacity estimate with the arithmetic shown — documents ingested per day, embedding spend, and vector-store growth over 12 months
- A measured cache hit rate over a replayed question log, with the latency and cost it saved
- A test that fails the primary provider mid-request and the log lines showing the fallback answering
- An architecture diagram annotated with what breaks first under load and what the design does about it
Signal it
Designed and prototyped a resilient RAG API over a few hundred regulatory PDFs — a measured cache hit rate cut per-query cost, and a tested provider-fallback path kept answers flowing through a simulated outage.
Interview
Questions you'll get asked
- Design a chatbot backend that needs to serve 10,000 concurrent users with sub-2s latency.
- How would you cache LLM responses without serving stale or unsafe answers?
- Design a system that ingests 1M documents a day into a RAG pipeline.
- Your model provider's API is down for 10 minutes — how does your system degrade gracefully?
- How would you rate-limit an API per customer while keeping p99 latency low?
- Walk me through the trade-offs of a synchronous vs async (queue + webhook) API for a slow AI task.
- How would you scale a vector search index from 1M to 100M embeddings?