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LLM Large
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A practical guide to AI engineering

Understand the systems.
Feel ready for the interview.

Make sense of LLMs, retrieval and AI architecture. Work through interview questions and design decisions, one topic at a time.

Practice interview questions Find a learning path

For engineers preparing for their next role — or getting better at the one they have.

THE ENGINEERING NOTEBOOK
01 / RETRIEVAL SYSTEMS

When a good model gives a bad answer.

Your RAG system retrieves relevant documents, but its answers still miss the point. Where would you look first?

RetrieveRerankGenerate
Start with the evidence.
Then explain the trade-offs.
Learn the foundationsPractice your reasoningDesign for the real world

Advanced interview lab

Go beyond the architecture diagram.

Work through RAG failures, KV-cache calculations, GPT-style training and large-scale serving. Eight lessons with worked answers, self-checks and primary sources.

Explore the library

What would you like to work on?

All learning topics
01

Models & inference

LLM engineering

Understand attention, fine-tuning, evaluation and inference.

02

Search & context

Retrieval & RAG

Work through chunking, retrieval, reranking and grounded answers.

03

Tools & orchestration

Agentic AI

Build agents with tools, memory, clear boundaries and guardrails.

04

Pipelines & platforms

Data engineering

Study streaming, Spark, lakehouses and distributed data systems.

05

Architecture & scale

System design

Practice capacity estimates, reliability and architecture trade-offs.

06

Algorithms & implementation

Coding practice

Solve problems, compare approaches and explain your reasoning.

Make a plan

A useful place to start

Choose your role and the time you have for a starter plan. Sign in for a plan that adapts to your progress.

Sign in to sync
Learn25 min

Review an LLM engineering topic

Read the explanation, then describe the idea in your own words.

Start
Practice15 min

Try five interview questions

Answer before opening the explanation. Note what you want to revisit.

Start
Apply30 min

Solve one coding problem

Implement your approach and explain its time and space complexity.

Start

From concepts to decisions

Talk through the hard parts.

Good system design takes more than a diagram. Practice explaining what you need, what you would build and what happens when it fails.

Explore system design
  1. 01

    Set the constraints

    Who uses the system? How much traffic, data and latency can it handle?

  2. 02

    Make the trade-offs

    Choose storage, retrieval and service boundaries. Explain what each choice costs.

  3. 03

    Test the failure cases

    Consider retries, stale data, outages and answers you cannot trust.

Keep learning

A little context goes a long way.

Read the research, follow engineering updates or suggest an improvement to the library.