Intermediate
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Level 4: AI and Building
Production AI, vibe coding, and language models. How AI systems actually work in production, how to direct AI …Level 3: The Infrastructure
Databases, servers, APIs, and the cloud. Where software runs, how it stores data, and how systems communicate …Tool Use (in Language Models)
The capability of a language model to invoke external tools: APIs, code execution, retrieval, computation: and …Structured Output
Constraining a language model to emit output that conforms to a specified schema (JSON, regex, grammar). The …Prompt Caching
Server-side caching of attention key/value tensors for repeated prompt prefixes, reducing latency and cost for …Model Context Protocol (MCP)
An open protocol that standardises how language models connect to tools, data sources, and external systems …LLM Routing
Architectures that direct each request to one of several available language models based on cost, capability, …Function Calling
Structured tool invocation by language models: how the model emits typed function calls, how runtimes execute …Chain-of-Thought (CoT) Prompting
Eliciting intermediate reasoning steps from language models to improve performance on multi-step problems, …GitHub Actions Security: Risks, Exploits, and Hardening
A comprehensive guide to GitHub Actions security vulnerabilities, common exploit patterns, and how to audit …Version Control Fundamentals and GitEverything as Code: Treating All Artifacts as Software
The principle of defining infrastructure, configuration, documentation, policy, video, and design as …Build a Code-Based Video: Programmatic Video Production with Remotion
A step-by-step guide to creating professional demo and explainer videos entirely in code using Remotion, …AI Systems Are Software Systems
Why production AI requires the same engineering discipline as any distributed system, and how this wiki covers …Tiered Analysis Pattern - Progressive Depth for AI Processing
Apply cheap analysis first, score results, then apply expensive analysis only to candidates that pass a …The Well-Architected Framework - Why Every Cloud Provider Has One
What the Well-Architected Framework is, its origins at AWS, how Azure and GCP adopted it, its six pillars, and …Sorting and Search Algorithms for AI Pipelines
How sorting and search algorithms underpin AI pipeline design: complexity trade-offs, partial sorting for …Retry and Backoff Patterns for AI Services
Exponential backoff with jitter, retry budgets, and idempotency patterns for production AI systems. Why AI …Prompt Engineering for Enterprise AI Applications
Practical prompt engineering patterns for production AI systems: system prompts, few-shot examples, …Programming Languages for AI - Python, TypeScript, HCL
A practical guide to the three languages used across a modern AI stack: Python for agents and models, …Hybrid Cloud
What hybrid cloud is, why it matters for AI workloads with data gravity and compliance constraints, and AWS …Hardware Constraints for AI Systems
CPU vs GPU, VRAM limits, memory bandwidth, and how hardware choices determine what AI models you can run and …Caching Patterns for AI Applications
Semantic caching, Anthropic prompt caching, response caching, and embedding caching for AI applications. Cost …AWS Well-Architected AI/ML Lens - Applying Best Practices to Machine Learning
The AWS ML Lens extends the Well-Architected Framework to cover ML lifecycle phases, ML pipeline automation, …
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