Context Engineering in Production #6
Keep sessions focused.
Good:
Auth → JWT → Claims → Refresh
Bad:
Auth → Terraform → Search → Databricks
Every topic jump carries old context into a new problem.
Focused sessions preserve signal.

Context Engineering in Production #6
Keep sessions focused.
Good:
Auth → JWT → Claims → Refresh
Bad:
Auth → Terraform → Search → Databricks
Every topic jump carries old context into a new problem.
Focused sessions preserve signal.
Context Engineering in Production #5
A coding session grows every turn.
Code.
History.
Tool logs.
Previous answers.
By turn 20, your new question may be tiny compared with everything traveling with it.
Context debt compounds.
Context Engineering in Production #4
Treat context like a budget.
0–40% 🟢 Healthy
40–50% 🟡 Watch
50–70% 🟠 Compress
70%+ 🔴 Reset
A bigger window doesn't remove the need for context discipline.
Context Engineering in Production #3
The cheapest token isn't a cached token.
It's the token that never enters the session.
Before optimizing models, ask:
Why are we sending this context at all?
Good retrieval removes noise before tokenization.
Context Engineering in Production #2
A 1M-token window doesn't mean you should fill it.
Capacity ≠ necessity.
100K tokens of noisy code can be worse than 5K tokens of relevant symbols.
The goal isn't maximum context.
It's minimum sufficient context.
Context Engineering in Production #1
Your question might be 10 words.
But the model may receive:
• code
• chat history
• tool output
• system instructions
The prompt is often the smallest part of the request.
Optimize what surrounds it.
Meta's Context Language Models edit context like a file, no training.
BrowseComp-Plus: +11.4% accuracy, 21.5% fewer FLOPs
vs the SOTA context strategy.
EdgeBench, 12 hours: +5% score, 59% fewer FLOPs.
The bill is context management, not token price. #ContextEngineering
One project-context update changed the reference files while leaving the working-state file byte-for-byte identical. I kept that distinction in the record: changing the references wasn't permission to rewrite the current state. #AI #ContextEngineering
Before you ask AI to write anything, paste four lines: your role, who you sell to, three sentences you've actually written, and one thing you never want it to sound like. Ninety seconds.
AI coding agents need more than good prompts.
Meet the Context Development Lifecycle (CDL): Generate, Evaluate, Distribute, Observe.
The Context Flywheel closes the loop:
💡 better context → better results → better observations → better context
🎬 Watch now: buff.ly/kIAuTeo
Four paragraphs of company background, retyped from memory, before I can ask one real question. New chat, same ritual, every time.
Then I saved the paragraph instead of retyping it. Same model, completely different output.
El mapa completo del context engineering en agentes IA 2026
Cargar todo el CLAUDE.md cuesta 20% mas y no mejora nada. Los 4 principios de context engineering agentes IA que confirman Anthropic y ETH Zurich en 2026
#contextengineering #agentesdeia #claudecode #promptengineering #anthropic
Generic AI output isn't a ceiling, it's a floor. Give the model your role, your audience, and three sentences in your actual voice, and the floor moves. Same request, completely different output. The model was never the limit.
AI is only as useful as the context around it. Rules, examples, documents and connected data help turn unreliable answers into work your team can review and act on.
The model isn't confused when it gives you generic output. It's doing exactly what happens when you skip context: it averages over everyone, for everyone, and hands you writing for no one in particular.
Context Engineering is more than writing a better prompt.
Learn more at GALTech School of Technology: www.galtechlearning.com
#ContextEngineering #AI #PromptEngineering #GenerativeAI #AILearning
The AI world spent years chasing bigger.
Now a different question is emerging: What if the advantage comes from better context?
Our latest Tech Bulletin explores SLMs, context engineering, and system-centric AI.
👇 Read it: zurl.co/JOC9b
Eleven forty at night, pasting the same brand doc into ChatGPT for the fourth time that day. Ten seconds a pop, a dozen times a day, every day. That's not fast work.
An LLM predicts the next word by averaging across everything it's read. Give it no context and it bets on the statistical middle of the internet, which is why the output sounds like nobody. Role, audience, voice, one real example.