So whats the next big fad .... "Context Engineering".
So WHAT IS CONTEXT ENGINEERING?
饾煭. It’s Feature Engineering—but for AI Agents.
(*Check what's feature engineering down below)
饾煯. The art of filling the context window with exactly what’s needed.
饾煰. You’re managing “working memory” like an operating system manages RAM.
饾煱. Agents need engineered context: instructions, tools, memories, examples, feedback.
饾煴. Poor context = forgotten steps, broken tools, bad decisions.
饾煵. Long-running agents hit context limits fast—engineering is essential.
饾煶. Vibe coding doesn’t scale—context engineering does.
*In the context of AI prompting, feature engineering refers to the process of creating, selecting, and transforming input data (features) to improve the performance of a machine learning model. It involves using domain expertise and various techniques to make the data more suitable for the AI model to learn from and generate better outputs.
Check the following links for context:
- Langchain Exaplanation on what it is:
https://youtu.be/4GiqzUHD5AA?si=BEIThE_HOT-i3I9T
- First post l saw talking about this by Andrej Karpathy (@karpathy):
https://x.com/karpathy/status/1937902205765607626
- And you may wish to follow this lady she dove right into it detail ... never mind hte course it hella expensive !:
https://x.com/MaryamMiradi/status/1940810454013518178
#contextengineering #featureengineering #aiagents #langchain #promptengineering #aioptimization #machinelearning #contextwindow #workingmemory #aiprompting #vibecoding #aiperformance #contextualization #aitraining #aiscaling
So whats the next big fad .... "Context Engineering".
So WHAT IS CONTEXT ENGINEERING?
饾煭. It’s Feature Engineering—but for AI Agents.
(*Check what's feature engineering down below)
饾煯. The art of filling the context window with exactly what’s needed.
饾煰. You’re managing “working memory” like an operating system manages RAM.
饾煱. Agents need engineered context: instructions, tools, memories, examples, feedback.
饾煴. Poor context = forgotten steps, broken tools, bad decisions.
饾煵. Long-running agents hit context limits fast—engineering is essential.
饾煶. Vibe coding doesn’t scale—context engineering does.
*In the context of AI prompting, feature engineering refers to the process of creating, selecting, and transforming input data (features) to improve the performance of a machine learning model. It involves using domain expertise and various techniques to make the data more suitable for the AI model to learn from and generate better outputs.
Check the following links for context:
- Langchain Exaplanation on what it is:
https://youtu.be/4GiqzUHD5AA?si=BEIThE_HOT-i3I9T
- First post l saw talking about this by Andrej Karpathy (@karpathy):
https://x.com/karpathy/status/1937902205765607626
- And you may wish to follow this lady she dove right into it detail ... never mind hte course it hella expensive !:
https://x.com/MaryamMiradi/status/1940810454013518178
#contextengineering #featureengineering #aiagents #langchain #promptengineering #aioptimization #machinelearning #contextwindow #workingmemory #aiprompting #vibecoding #aiperformance #contextualization #aitraining #aiscaling