Prompting got us in the door. Context engineering is what will keep us in the room.
The last two years were all about prompt crafting.
Clever hacks, structured formats, and “magic” one-liners. But if you’ve been building AI workflows seriously - especially agents or copilots - you’ve likely hit a hard wall:
Your outputs are unpredictable, unscalable, or flat-out wrong.
Welcome to the real challenge: Context Engineering.
What is Context Engineering?
At its core, context engineering is the art and science of controlling what the model sees - and how it sees it.
It's not just what you feed the model (documents, examples, metadata), but also:
How you retrieve and prioritize information
How you compose multi-step reasoning chains
How you manage memory and state
How your context changes over time
This is the invisible architecture behind high-performing AI systems. And the better your context, the better your model behaves.
Why Prompting Isn’t Enough
Prompting is static. Context is dynamic.
Prompting assumes a fixed frame of reference.
Context engineering builds that frame, and updates it as your system interacts with the world.
LangChain calls this "Context-Driven Agents" - models that can not only think, but also access, evaluate, and use the proper context at the right moment.
“Context engineering is how we move from clever demos to real products.”
Real-World Context Engineering Looks Like This:
Here’s what separates basic apps from AI-native systems:
❌ Bad Context
Static prompt
Manual RAG from 1000s of docs
No memory
One-shot output
✅ Engineered Context
Context windows updated via event triggers
Intelligent retrieval with hybrid search, metadata filters
Persistent state tracked across sessions
Multi-step tool use with context chaining
You don’t just inject data - you design a system that learns what’s relevant and remembers what matters.
3 Emerging Patterns in Context Engineering
Structured Retrieval Pipelines Retrieval-Augmented Generation (RAG) is no longer enough. Engineers now chain together multiple retrievers, filters, re-rankers, and evaluators to get precision.
Long-Term Memory Loops: LangChain and open-source agent frameworks now enable agents to build persistent knowledge graphs from past interactions, allowing for compounding intelligence.
Failure-Resistant Context Stacks As Dan Breunig explains, most AI failures aren’t from bad models - they’re from misaligned or missing context. The fix? Use LLMs to verify and refine their context before executing the main task.
Why This Matters for Builders
If you're building:
Copilots for internal teams
AI agents for ops, marketing, sales, or research
Customer-facing bots
Your success won’t depend on prompts. It’ll depend on how well you can build, scale, and govern your context stack.
This is the new leverage layer.
What You Can Do Now
Map out your context architecture What data is your model seeing? What’s missing? Where does it fail?
Design context flows, not just prompts Think like a systems engineer. Inputs, filters, retrieval strategies, memory - it's all part of the design.
Track context failures - log not just outputs, but context state at each step. Most bugs live there.
Make context composable Use tools like LangGraph or ReAct-based flows to modularize context logic and avoid prompt sprawl.
