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Context Engineering Webinar: Everything You Missed
Thanks to everyone who joined our first Context Engineering Webinar!
If you missed it, don’t worry, you can watch the replay here and join the Context Engineers Discord Server to keep learning and connecting with the community.
Speakers and Contact Info
For follow-up questions or collaborations, feel free to reach out:
Ghita Houir Alami (ZeroEntropy) — LinkedIn | Email | Twitter | Schedule a call
Join the Community
The discussion around Context Engineering, RAG, and Agentic Search is just beginning.
Join the Context Engineers Discord to share experiments, ask questions, and connect with others building the next generation of AI systems.
What is Context Engineering?
We can all agree that LLMs are incredible — but they also have real limitations. There’s a lot of buzz around growing context windows, yet scaling context size doesn’t mean scaling intelligence. In fact, the opposite often happens.
As models see more and more text, “context rot” sets in — the model’s effective understanding gets diluted. This not only reduces quality but also makes responses slower and more expensive.
That’s where Context Engineering comes in.
It’s the iterative process of designing, updating, and optimizing what an LLM sees at any given moment — even across multiple reasoning steps.
This process combines:
In-context learning (instructions, few-shot examples),
Retrieval (from a knowledge base, memory, or an external API),
And context flow — where agents not only read from memory but also write back summaries or updates for future use.
In practice, this looks like:
Search everywhere, but with intelligence.
The Talk: “RAG vs The Agent Loop — A Fake Dichotomy”
In my talk for ZeroEntropy, I explored a common misconception: that Retrieval-Augmented Generation (RAG) and Agentic Search are opposing approaches.
They’re not. The truth is, they complement each other.
I broke down why “RAG is dead” is an oversimplification. For most production systems, hybrid retrieval plus reranking remains the most accurate, cost-efficient, and stable foundation — even for agents.
Key takeaways:
Not all context helps. Retrieval remains critical.
Accuracy beats iteration. Optimizing search quality gives far better ROI than running multiple noisy agentic passes.
Two-stage retrieval wins. First maximize recall, then precision. Find the hay, then the needle.
Reranking matters. It boosts precision and NDCG, and often cuts the number of passes an agent needs.
Our experiments showed that Hybrid + Rerank pipelines often find the right answer in one shot — while weaker searches force agents into longer, costlier loops. Still, in deep research or multi-hop reasoning tasks, agentic loops remain valuable when paired with strong retrieval tools.
If you want to explore this further, our full Context Engineering Cookbook is available in the ZeroEntropy documentation here.
Context Engineers Community
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