AI Engineering Practice

Build the parts of an AI application yourself: prompts and evaluation, retrieval, memory and context, and agents that call tools.

6 paths · 66 challenges

More soon
agentic-full-stack-web-development
Agentic Full-Stack Web Development
4 challenges · 1 module
DjangoDjango.Net.NetMernMernReactReactSpringbootSpringboot
The Agent-Ready Repository
Not startedStart
memory-context
AI Context & Memory Engineering
10 challenges · 2 modules
linux-cliLinux CLI
Context Management
Not startedStart
ai-agents
Build an AI Agent From Scratch
23 challenges · 4 modules
linux-cliLinux CLI
Tool Calling & Agent Loop
Not startedStart
More soon
mcp
Model Context Protocol (MCP)
5 challenges · 1 module
linux-cliLinux CLI
Server Tools, Resources & Prompts
Not startedStart
prompt-engineering
Prompting & Evaluation
12 challenges · 2 modules
Linux CLILinux CLI
Prompt Engineering
Not startedStart
More soon
rag
Retrieval-Augmented Generation (RAG)
12 challenges · 2 modules
python-312Python 3.13
Embeddings & Vector Search
Not startedStart

From the blog

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Model Context Protocol Update: Stateless MCP Is HereModel Context Protocol’s latest update replaces session-bound transport with a stateless core, MRTR, routing headers, and sharper migration boundaries.

Frequently asked questions

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Which paths are in this category?

6 paths: Agentic Full-Stack Web Development (4 challenges), AI Context & Memory Engineering (10 challenges), Build an AI Agent From Scratch (23 challenges), Model Context Protocol (MCP) (5 challenges), Prompting & Evaluation (12 challenges) and Retrieval-Augmented Generation (RAG) (12 challenges).

How many challenges are there?

66 challenges across 12 modules.

Do I need to install anything?

No. Every challenge runs in a browser-based IDE with everything already set up, and your code is graded automatically against a test suite.

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