AI System Design Course
AI system design is about the architecture around the model: retrieval, routing, caching, guardrails, fallbacks and the cost and latency tradeoffs between them. This course works through real designs end to end, from a support assistant at ten thousand requests a day to an agent platform with many tools, and covers the interview questions that come with them.
AI system design is about the architecture around the model: retrieval, routing, caching, guardrails, fallbacks and the cost and latency tradeoffs between them. This course works through real designs end to end, from a support assistant at ten thousand requests a day to an agent platform with many tools, and covers the interview questions that come with them.
- Real designs, worked end to end
- Cost and latency tradeoffs
- Guardrails and failure containment
- Interview question practice
- Modules
- 8
- Format
- Online
- Mentorship
- Weekly 1:1
- Project
- Production
Why this track
What you get that a general AI course does not cover.
The model is the smallest part of the system
Most of the engineering that determines whether an AI feature is good, cheap and reliable happens around the model, and that is exactly what system design interviews test.
Tradeoffs, not recipes
Every architecture choice here is presented with what it costs and what it buys, so you can defend a decision in an interview instead of reciting one design.
Failure containment by design
Guardrails, fallbacks, timeouts and circuit breakers are placed deliberately, so a bad generation degrades the experience instead of ending it.
Interview-ready and production-relevant
The same designs that appear in interviews are the ones you will defend at work, and both are covered without pretending the interview version is the real one.
Curriculum
Eight modules, each ending in something you can show.
- M01
Designing for an LLM, not a database
What changes when the component you are calling is nondeterministic, slow and billed per token. Requirements gathering for AI systems.
- Nondeterminism
- Latency budgets
- Quality requirements
- Capacity planning
Tools
- Excalidraw
- Python
- M02
Prompt and context architecture
System instruction layering, context assembly, ordering, compression and the token budget you allocate across each part.
- Instruction hierarchy
- Context assembly
- Compression
- Token budgets
Tools
- Anthropic
- OpenAI
- M03
Retrieval architecture
Ingestion pipelines, chunking strategy, hybrid search, reranking and evaluating retrieval separately from generation.
- Ingestion
- Chunking
- Hybrid search
- Reranking
Tools
- pgvector
- Elasticsearch
- Cohere
- M04
Routing, cascades and model choice
Sending easy queries to cheap models and hard ones to expensive ones, with classifiers and confidence signals to decide.
- Model cascades
- Classifiers
- Confidence signals
- Quality vs cost
Tools
- LiteLLM
- M05
Caching layers
Semantic caches, exact caches, prompt caching and context caching, including invalidation and the correctness risk of each.
- Semantic cache
- Prompt caching
- Invalidation
- Staleness
Tools
- Redis
- GPTCache
- M06
Guardrails and output validation
Input filtering, output validation, PII handling, refusal behaviour and structured output enforcement at the boundary.
- Input filtering
- PII
- Output validation
- Refusals
Tools
- Guardrails AI
- Pydantic
- M07
Reliability and graceful degradation
Timeouts, retries with jitter, circuit breakers, provider fallbacks and queueing so a spike does not take the service down.
- Timeouts
- Circuit breakers
- Fallbacks
- Backpressure
Tools
- Envoy
- Celery
- M08
Interview practice and capstone
Timed design problems with a worked reference answer, then a full system design of an AI product you own end to end.
- Design questions
- Whiteboard practice
- Tradeoff defence
- Capstone review
Tools
- Excalidraw
Roles this maps to
Where this track lines up with real job descriptions.
AI Systems Architect
Own the architecture of AI features, balancing quality, latency and cost against a real traffic profile.
Senior Backend Engineer
Bring distributed systems fundamentals to LLM features, where the hard parts are probabilistic.
Machine Learning Engineer
Take a model from notebook to a serving architecture other people can operate.
AI Engineer
Design the whole path from user request to grounded, validated, costed response.
What you can do afterwards
Concrete capabilities, not a certificate.
- Break a feature into components with explicit quality, latency and cost budgets
- Design retrieval that is evaluated independently from generation
- Justify routing, caching and cascade decisions against real numbers
- Place guardrails and fallbacks so bad generations degrade rather than break
- Answer AI design interview questions with defensible tradeoffs
Questions
Do I need strong distributed systems background?
Some helps, but we build the concepts as we go. What matters more is comfort reasoning about tradeoffs rather than knowing a specific technology.
Are the numbers in the module summaries real?
They are typical production figures for the workload described, not measurements from this specific product. We treat them as the kind of number you would establish yourself during design.
Will this help in system design interviews?
Yes, for AI-flavoured product design, which is increasingly common. It will not cover classical data-storage design, so pair it with general system design practice.
How much maths is involved?
Enough to estimate token counts, costs and latency. No linear algebra, and no calculus.
Related tracks
Ready when you are
Applications for the next cohort are open. You pick your track during the first month, so you can change if the material does not suit you.