Production AI
for teams that ship.

Architecture patterns, implementation guides, and tool references. Built for engineers turning AI into working systems.

New to this? Start at Zero → · Find your path → · Look up a term →

The AI Stack

Where every article fits.

Every guide, pattern, and tool review maps to one of these five layers. The wiki covers all of them. Understanding the stack helps you know what to read next and why each layer depends on the one below it.

Layer 05 Applications What users interact with
What users interact with Interfaces, workflows, and AI-powered products end users see directly. Industry verticals, chatbots, search, and classification pipelines.
Industry Solutions Case Patterns RAG Search Agent Systems Chatbots Dashboards Classification
Layer 04 Orchestration How models are directed
How models are directed Prompt patterns, agentic loops, tool calling, memory, and routing logic that control model behaviour at runtime.
Patterns Guides Prompt Engineering Agentic Loops Tool Calling Memory Routing
Layer 03 Models The intelligence layer
The intelligence layer Foundation models, fine-tuning strategies, embedding techniques, evaluation frameworks, and inference optimisation.
Tool Reviews Comparisons Foundation Models Embeddings Fine-tuning Evaluation Inference
Layer 02 Data What models learn from
What models learn from Ingestion pipelines, vector stores, feature engineering, preprocessing, and data lineage for AI systems.
Guides Glossary Lineage Pipelines Vector Stores Preprocessing Feature Stores
Layer 01 Infrastructure Where everything runs
Where everything runs Cloud providers, deployment frameworks, APIs, security controls, observability, and cost governance for AI workloads.
Frameworks Tools Cloud / AWS Security Monitoring APIs Cost Management

Data flows up. Governance and compliance apply to every layer.

01. Browse by section.
174
How-to Guides Step-by-step tutorials covering AI architecture, testing, MLOps, and production deployment.
175
Tool Reviews Deep dives into AWS AI services, LLM platforms, and developer frameworks.
102
Patterns Reusable technical patterns for RAG, agents, pipelines, and production AI systems.
74
Comparisons Side-by-side analysis to help you choose the right tools and approaches.
58
Frameworks Governance, ethics, regulatory, and methodology frameworks for AI teams.
442
Glossary Plain-English definitions for AI, ML, and cloud engineering terminology.
25
Case Patterns Real-world AI implementation patterns drawn from production use cases.
105
Industry Solutions AI applications for media, finance, insurance, geospatial, and more.
Layers The foundational layers of AI systems, from data and models to orchestration.
Architecture System design patterns and reference architectures for scalable AI infrastructure.
Build Hands-on project guides for building real AI systems from architecture to working code.
Getting Started Core concepts, setup guides, and a clear path from zero to working.
02. Start here.

Staff picks

A cylinder projecting rows of red data points, representing document retrieval and indexing in a RAG pipeline.
Guide Building RAG Systems End-to-end retrieval-augmented generation: document ingestion, embedding, vector search, and ranked responses in production. Read the guide →
A silhouette facing a red-lit industrial tower representing regulatory oversight and compliance architecture.
Framework EU AI Act Risk Framework Classify your AI system under the EU AI Act. Understand what risk tier applies and which obligations follow from it. Read the framework →
Dark industrial gears under red light representing the mechanics of load planning and system capacity.
Guide Capacity Planning for AI GPU, memory, throughput, and cost estimation for LLM workloads. Before you commit to infrastructure. Read the guide →
03. Find your path.

Who is this for

Product Manager Understand what engineers are building, how long it takes, and when the AI approach is wrong. Start reading → Founder Decide what to build, who to hire, and whether the approach your team proposes will work. Start reading → Vibe Coder You direct, AI writes. Know enough to debug, deploy, and keep the system running. Start reading → Consultant Speak AI fluently: governance vocabulary, risk frameworks, and architecture evaluation. Start reading → Finance and Business Evaluate proposals, build a business case, and understand the regulatory obligations that apply. Start reading → Student or Switcher Start from zero. Computers, internet, code, then AI in a sequence that actually makes sense. Start reading →

The learning path

Five levels, one clear direction.

Every article is assigned a level. You always know what comes before it and what comes next. Level 0 assumes no technical background. Level 4 covers production AI systems at scale.

Level 0 Computers and the Internet How hardware, networks, and the web actually work. No prior knowledge assumed.
→
Level 1 and 2 Code and Collaboration Terminals, logic, Git, APIs, and version control.
→
Level 3 Cloud and Infrastructure Databases, cloud services, APIs, and scalable system design.
→
Level 4 AI and Production Systems LLMs, RAG, agents, MLOps, and shipping AI to production.
Start at Level 0 View all learning paths
04. Recently added.

From the wiki

Through The Juggler May 2026 Through The Fashionista May 2026 Through The Craftsperson May 2026 Through Juggling and the Brain May 2026 Through Juggling and Technology May 2026 Through Juggling and Change May 2026 Levels Level 4: AI and Building May 2026 Levels Level 3: The Infrastructure May 2026 Levels Level 2: Managing Work May 2026 Levels Level 1: How Code Works May 2026 Levels Level 0: The Foundation May 2026 For For Vibe Coders May 2026

Quick access

Start at Zero No coding background needed
Look up a term 442 plain-English definitions
Compare tools 74 side-by-side breakdowns
Your learning path Guides tailored to your role

Browse by topic

Full library
RAG LLMs AI Agents MLOps AWS Vector Databases Embeddings Prompt Engineering Fine-tuning EU AI Act Data Pipelines Monitoring FastAPI AWS Bedrock Claude Testing Security ISO 42001 LangChain Kubernetes Transformers Inference Cost Optimisation Architecture

Open source

Tools built from the same experience.

Freelancer Templates and Freelancer Automation grew out of the same real-project work that powers this wiki. Both are free to use.

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