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– Favorite AI coding tools: Cloud Code, Codex, and Google’s Anti-Gravity.
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Classroom Contents
Stop Vibe Coding - Context Engineering and RAG for AI Agents
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- 1 – Introduction: The challenge of too much “fluff” in the AI space and how to focus on what matters.
- 2 – Meet Cole Meine: Background, expertise, and his mission in applied AI.
- 3 – What listeners will learn: Context engineering, RAG, and moving workflows to production.
- 4 – The origin of context engineering: Why treating prompts and context as engineered resources matters.
- 5 – Vibe coding vs. context engineering: The importance of specificity and reducing assumptions.
- 6 – Practical steps for context engineering: Mindset shift, planning, and using AI to ask clarifying questions.
- 7 – Success criteria and user journeys: How to define what “done” looks like for AI projects.
- 8 – How much time to spend on planning: Product requirement docs and upfront investment.
- 9 – Favorite AI coding tools: Cloud Code, Codex, and Google’s Anti-Gravity.
- 10 – Staying up to date in AI: Research strategies and the value of community.
- 11 – Introduction to RAG Retrieval Augmented Generation: What it is and why it matters.
- 12 – How RAG works: Embedding models, vector databases, and semantic search.
- 13 – Metadata filtering in RAG: Multi-tenancy, hierarchical search, and business use cases.
- 14 – Handling messy data: ETL/ELT pipelines and preparing data for AI agents.
- 15 – Scaling workflows: Moving from n8n prototypes to production code Python/TypeScript.
- 16 – Deployment strategies: Frontend, backend, and cloud hosting options.
- 17 – The importance of version control: Using GitHub for safe states and CI/CD.
- 18 – Final advice: Start simple, build your process, and customize your system.
- 19 – Where to find more: Cole Meine’s YouTube channel for more on RAG and context engineering.
