AI Engineering Lab: Knowledge Base
The distilled concepts behind the 24-week program. The curriculum points here for depth; you can also browse it standalone as a reference library.
How this knowledge base is built. Original synthesis by Zorost Intelligence, researched from official vendor documentation and primary sources. See
research/for the raw research notes and14-references-bibliography.mdfor the full bibliography.
The concepts
| # | File | Covers |
|---|---|---|
| 01 | 01-ai-engineering-discipline.md | The AI engineering discipline: Ng's four skills, three loops, roles, and how the program maps to them |
| 02 | 02-ml-dl-fundamentals.md | ML & deep learning fundamentals: supervised learning, neural nets, training, evaluation |
| 03 | 03-llm-core-concepts.md | Tokens, embeddings, attention, transformers, KV cache, MoE, generation |
| 04 | 04-prompt-context-engineering.md | Prompt engineering and context-window engineering: budgets, compaction, caching, structure |
| 05 | 05-rag-graph-engineering.md | RAG, vector search, chunking, reranking, citations, and knowledge-graph engineering |
| 06 | 06-model-engineering.md | Quantization, fine-tuning (SFT/DPO/LoRA), distillation, serving, and selection order |
| 07 | 07-evals-error-analysis.md | Evals, LLM-as-judge, traces, human-level parity, error clustering |
| 08 | 08-local-inference-gpu.md | Running models locally: Ollama, llama.cpp, MLX, CUDA/MPS/ROCm, VRAM planning |
| 09 | 09-harnesses-tools.md | Coding-agent harnesses and routers: Claude Code, Cursor, OpenCode, DeepSeek Harness, OpenRouter |
| 10 | 10-agents-multiagent.md | Agent fundamentals, multi-agent orchestration, memory, guardrails |
| 11 | 11-mcp-ecosystem.md | MCP, A2A, and the agent connectivity ecosystem |
| 12 | 12-cloud-platforms.md | Azure AI Foundry, Google Vertex AI, AWS Bedrock: capabilities and comparison |
| 13 | 13-databricks-overview.md | The Databricks Data Intelligence Platform at a glance |
| 14 | 14-references-bibliography.md | Textbooks, papers, letters, and vendor docs the program draws from |
Research notes (raw)
| File | Topic |
|---|---|
| research/00-zorost-brand-notes.md | Zorost Intelligence branding collected from zorost.com |
| research/01-source-repo-analysis.md | Analysis of a representative open LLM-engineering course repo |
| research/02-ng-framework-textbooks.md | Ng's skills map & letters, DeepLearning.AI catalog, textbooks |
| research/03-harnesses-local-stacks.md | Harnesses, routers, local inference, quantization, fine-tuning stacks |
| research/04-agents-ecosystem.md | Agents, multi-agent, MCP/A2A, OpenClaw, Hermes, frameworks |
| research/05-cloud-platforms.md | Azure AI Foundry, Google Vertex AI, AWS Bedrock deep research |
| research/06-databricks-deep-dive.md | Databricks zero-to-hero research (the Databricks module backbone) |
| research/07-zorost-skills-map-guide.md | Zorost's own published analysis of Ng's skills map (six-block programme, systems spine) |
| research/08-zorost-fieldwork-notes.md | Zorost AI Fieldwork: methodology, experiments, reporting standard |
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