The context engineering learning hub (NotebookLM)

Curated sources, audio overviews, and a chat interface to explore the future of AI work

Yesterday's newsletter on context engineering got a lot of attention. Makes sense as it's a major concept that's just starting to pick up steam.

I spent some time collecting the best sources available right now on context engineering and loading them into a NotebookLM. This gives you a searchable, conversational knowledge base on the topic.

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Fair warning about the technical content

Many of these sources were written and created for developers and AI engineers. You'll encounter terms like:

• Agent architectures

• Token optimization

• RAG pipelines

• Context windows

Don't let that scare you.

Here's why it's actually valuable.

When they talk about "context failures," think about AI producing generic content because it lacks client-specific knowledge.

When they discuss "context ordering," consider how you organize information in your project files.

When they mention "structured outputs," that's about getting consistent results across different content pieces.

The terminology is technical. The principles are universal.

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Getting the most from this NotebookLM

Start with the briefing documents for an overview. Then use the chat feature to translate concepts to your specific needs.

Questions that work well:

• "How does context pruning apply to content creation?"

• "What's the writer's version of dynamic context construction?"

• "Can you explain [technical concept] for someone who writes, not codes?"

• "How would a ghostwriter use these principles?"

Then, you can explore the mind map.

The AI will help bridge the gap between technical knowledge and practical writing applications.

Here's a quote from one of the sources: "Most agent failures are not model failures anymore, they are context failures."

Think about what this means for us. When ChatGPT writes generic garbage, it's rarely because the model isn't capable. It's because we haven't engineered the right context.

That's what makes this shift from prompt engineering to context engineering so important.

Here’s a look at what's in this knowledge base:

• Dynamic context systems that adapt to different tasks

• Why information structure matters more than volume

• The evolution from static prompts to engineered contexts

• Actual frameworks being deployed right now

While others are still trading prompt templates, you're learning the underlying architecture.

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Get instant access to the NotebookLM

Here is your access link to the NotebookLM on context engineering: