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Prompt Engineering may not be the final destination.
A prompt tells AI what you want.
Context helps AI understand why, for whom, under what constraints, using what information, and toward what outcome.
That's a significant difference.
Consider:
PROMPT
“Analyze this business.”
versus:
CONTEXT
Business objective
company background
financial data
operational constraints
customer information
decision criteria
desired outcome
human review requirements
Now the AI isn't simply receiving an instruction.
It is operating inside a designed environment.
That's why I'm exploring Context Engineering through a framework I'm calling Contextos™.
My current working model:
CONTEXT → AI → ANALYSIS → HUMAN REVIEW → DECISION → ACTION
I'm curious:
Do you think context will become a more important AI skill than prompting?
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This week, I have been working on automating my boss's inbox management, delegating email triage to AI while I handle reviews and approvals that require sound judgment. Because one wrong response to a client can cost a business revenue, I wanted to build a system that saves time without handing over critical decisions to AI.
How I built it:
I created two connected automation scenarios in Make.com.
Scenario 1: Intelligent Inbox Triage:
AI analyzes incoming emails, categorizes them by priority, summarizes their content, and recommends the next action. This makes it easier to identify urgent messages and decide what needs attention first.
Scenario 2: Human Review & Approval:
The workflow supports the review and approval process before an email is sent. Slack notifications help bring relevant updates to my attention, so I can review the proposed action and approve the response before it goes out.
Below are snippets of the two scenarios, including a Slack notification, showing how the workflow operates.
My takeaway? AI can help manage the inbox, but when a response could affect revenue or a client relationship, the final decision should remain human.
I'd test the triage with urgent emails that sound casual, such as 'I still can't access the account.' Do you review a sample of low-priority messages to catch urgent ones the AI missed?
Bengali Language Evaluation for AI systems.
AI models are becoming more and more competent -Language Evaluation involves more than checking whether a sentence is grammatically correct .AI need language that is context-sensitive and natural and also culturally appropriate.
I can contribute to projects based on Bengali Language Evaluation that requires-
•Semantic Accuracy and ContextualMeaning.
•Ambiguity Identification and Interpretation.
•Register tone and naturalness.
•Bengali-English linguistic interpretation and content quality.
•Comparative Evaluation and Response ranking.
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