AI does not work in a vacuum. Give it the rules, examples, documents and connected data behind the work. Better context leads to more consistent output—and a system built around how your team actually works.

AI does not work in a vacuum. Give it the rules, examples, documents and connected data behind the work. Better context leads to more consistent output—and a system built around how your team actually works.
What is #ContextEngineering, really?
Birgitta Böckeler breaks down the core components - instructions, conventions, tools, and MCP servers - along with the non-deterministic challenge every developer faces when relying on LLMs to load them.
🎥 Watch now: buff.ly/eVmX85i
#StrandsAgents #AIAgents #ContextEngineering #AmazonBedrock
There are a number of AI harnesses out there now. You have likely used coding harnesses like Claude Code or Codex.
Most developers build #RAG for semantic similarity. But similar ≠ relevant!
Your coding agent can retrieve documents that match the prompt word-for-word - without actually getting the context it needs to solve the problem.
🔗 See how #ContextEngineering bridges the gap: ⇨ buff.ly/dbdIicm
#Claude #Code now reads AGENTS.md (when CLAUDE.md) is missing
#AI #Agents #CodingAgent #LLM #ContextEngineering
github.com/anthropics/c...
AI works better with the right context: your rules, examples, documents and connected data. We map the workflow, then build a system around the way your team works. #ContextEngineering #WorkflowAutomation
Bad AI output usually isn't a prompt problem. It's a missing-person problem. The model doesn't know your role, your audience, or your voice, so it writes to nobody. Give it a real example of you before you ask for the draft.
Build enterprise context pipelines for AI - retrieval, ranking, filtering, compression, access control and prompt assembly with AWS examples. #contextengineering
AI works better with the right context. Clear rules, examples, documents and connected data can turn unreliable answers into a workflow your team can review and use. We map the work, then build around it. #ContextEngineering #WorkflowAutomation
Type just a question mark into a new chat and hit enter. Watch what happens: it won't ask you anything, it'll just guess a topic and run with it. That's what every blank prompt does.
I pasted the same four paragraphs of context into a fresh chat about a dozen times a day and never counted it as work. It is work. It's the same setup, on repeat, forever, because the model forgets you the second the chat closes.
AI can move quickly and still miss the mark without business context. Start with the rules, examples, documents and connected data that shape how your team works. Then build around it. People still decide. #ContextEngineering #WorkflowAutomation
You'd never hand a new hire a laptop and say 'go write the homepage,' no examples, no context. But that's what most people do to their AI tool every single day. Write the onboarding once. Role, audience, voice, standards.
内容平台 Sanity 这周上线了一个好设计:Agent Context。 思路很简单也很对: 与其让 Agent 整段读取内容(费 token、易跑偏), 不如给它一个结构化入口——直接查内容 schema, 拿到字段、类型、关系这些元信息。 把 schema 当生产代码来管👇🧵 #AIAgent #ContextEngineering
AI rarely fails in a vacuum. It fails when the workflow lacks the rules, examples, documents, or connected data that make an answer useful. Build the context first. Then automate what your team can trust. #ContextEngineering #WorkflowAutomation
Zu wenig #Content und #ContextEngineering. Da wird dann ein KI-System implementiert, das Unternehmenswissen liegt jedoch so heterogen vor, wie gehabt. PDFs, Docx etc. und alte Inhalte. Und heraus kommt Shit in/shit out. Aber zum Glück gibt es ja Leute wie mich, die das reparieren.
Stop obsessing over "Prompt Engineering.
The real skill in 2026 is "Context Engineering." Giving AI the right data, memory, and tools is what unlocks its true potential. Prompts are just the steering wheel; context is the engine. 🧠⚙️
Are you focusing on context yet?
#ContextEngineering #TechTrends
AI works better when it understands the work around it: your rules, examples, documents and data. We map the workflow, then build a system that prepares the work—while people still decide. #ContextEngineering #WorkflowAutomation
My current description of SOTA LLMs? They're like savant kids with tunnel vision. Very good once they're settled in and pointed in the right direction, but wildly unpredictable if not.
AI works better with the right context: your rules, examples, documents and workflow. We map how work happens, then build a system around it—so outputs fit the way your team works. #ContextEngineering #BusinessAutomation