Learning workspace

Continue building production AI judgment.

Follow prerequisite-safe paths, practice decisions in deterministic labs, and turn learning into exportable project evidence. Progress remains private in this browser.

A connected production AI learning route linking concepts, decisions, labs, evaluation, and deployment.

Active path

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0 of 37 concepts

Mastered

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Weakest evidence: activity 0%

Labs completed

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Of 15 available

Projects active

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Of 7 tracks

Active path

Production LLM Engineer
Open path
0 of 37 concepts0%
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concept

Tokens and Tokenization

How model inputs become discrete identifiers and why token boundaries affect cost and meaning.

Why: This is the next prerequisite-ready concept in the active path.

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Milestones

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Application contracts

Establish model interaction output tool and context contracts.

0%

Grounded product

Connect retrieval provenance generation and full-stack product boundaries.

0%

Operating system

Gate and operate the product with testing tracing security and economics.

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Applied evidence

Demonstrate the path through current assessments, lab attempts, and project evidence.

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Learning paths

8 paths · 38 domains
18h
0%

LLM Systems Foundations

Build a connected mental model from tokens through retrieval evaluation security and operations.

0/31 required evidence gates

Details
active
0%

Production LLM Engineer

Design an observable full-stack LLM product from capability validation through interfaces retrieval evaluation security and operations.

0/43 required evidence gates

Details
25h
0%

RAG Engineer

Build evidence-carrying retrieval systems from ingestion and provenance through search reranking grounded generation and evaluation.

0/26 required evidence gates

Details
29h
0%

Agentic Systems Engineer

Engineer bounded tool-using systems with explicit workflows state protocols evaluation observability security and human control.

0/31 required evidence gates

Details
22h
0%

AI Evaluation and Reliability

Turn product intent and risk into representative evaluations release gates traces service targets and continuously improving regression evidence.

0/21 required evidence gates

Details
23h
0%

Model Serving and Inference

Understand inference engines memory scheduling batching routing scaling distributed execution lifecycle controls observability and economics.

0/23 required evidence gates

Details
12h
0%

Ontology & Semantic Systems Engineering

Master formal semantic modeling, RDF/OWL triple stores, Domain-Driven Design integration, enterprise Data Mesh virtualization, and Neuro-Symbolic GraphRAG.

0/9 required evidence gates

Details
24h
0%

Blockchain & Decentralized AI Systems Engineer

Learn how blockchain systems execute and reach consensus, build and secure smart contracts, reason about rollups and data availability, understand zero-knowledge and verifiable computation, evaluate decentralized AI compute/storage systems, and design AI agents capable of safely interacting with programmable wallets and on-chain protocols.

0/12 required evidence gates

Details

Curriculum Mastery Heatmap (532 Concepts)

Visual completion grid across 6 production AI engineering domains.

Completed
Pending
Domain 01: Model Architecture & Systems0%
0 / 554 Concepts
Domain 02: Inference & Optimization0%
0 / 0 Concepts
Domain 03: RAG & Knowledge Systems0%
0 / 0 Concepts
Domain 04: Agents & Workflows0%
0 / 0 Concepts
Domain 05: Evals, Safety & Governance0%
0 / 0 Concepts
Domain 06: Operations & Infra0%
0 / 0 Concepts