مهندسی کانتکست هوش مصنوعی

عنوان دوره : Courses – AI Context Engineering [INDUSTRY PROFESSIONAL PLAN]

سطح : مبتدی

زمان کل دوره : 24 ساعت و 19 دقیقه

تعداد فايل ها : 11 (1080p: 8، 720p: 2، 654p: 1)

سال آموزش : 2026

فایل تمرینی : دارد: HTML (1)، IPYNB (6)، PDF (18)، TXT (10)

مربی دوره : –

نرم افزار : –

زیرنویس انگلیسی : ندارد

Courses – AI Context Engineering [INDUSTRY PROFESSIONAL PLAN]

AI Context Engineering

Master the art of AI-driven software development by learning to engineer context for large language models, build dynamic agents, and implement advanced RAG architectures.
View course on Vizuara
InstructorTeam Vizuara
FormatIndustry Professional Plan
Sessions10 Modules
AudienceIndustry Professionals
Course Description

AI Context Engineering is designed for professionals seeking to deepen their understanding of AI-driven software development. This course bridges the gap between traditional programming and modern neural network paradigms.

Participants will explore industry trends, practical implementations of large language models, and real-world applications including dynamic personal agents and cost-effective voice agents.
Learning Outcomes

By the end of this course, you will be able to:

Architect and deploy advanced RAG pipelines for LLMs.
Implement memory management strategies for persistent AI agents.
Utilize Model Context Protocol (MCP) to integrate external tools.
Optimize AI performance through effective context compression and isolation.
Build and automate complex workflows using modern AI development tools.

Prerequisites

Foundational knowledge of software development principles.
Basic familiarity with Python programming.
Interest in AI-driven software architecture.

Course Sessions
Module 1
01
Foundations of Context Engineering

An introduction to the evolution of software paradigms and the role of context in modern AI development.
Focuses on the shift from traditional programming to neural networks.

Core Topics

AI Paradigms
Context Engineering Overview
System Prompts

Resources & Assignments
Lecture Video
Miro Notes
Interactive Quiz
Colab
Module 2
02
The Instructional Layer

Designing adaptive instruction engines to guide LLM behavior effectively.
Practical design exercises for instruction-based AI systems.

Core Topics

Instruction Design
Adaptive Engines
Prompt Engineering

Resources & Assignments
Lecture Video
Miro Notes
Colab
Module 3
03
WRITE and SELECT: RAG Architectures

Deep dive into Retrieval-Augmented Generation (RAG) pipelines and data selection strategies.
Building a complete RAG pipeline using ChromaDB.

Core Topics

RAG Pipelines
Data Retrieval
Vector Databases

Resources & Assignments
Lecture Video
Miro Notes
Colab
Module 4
04
Tools and MCP

Integrating external tools and the Model Context Protocol (MCP) into AI workflows.
Focuses on tool-use and agentic capabilities.

Core Topics

MCP
Tool Integration
Blender Integration

Resources & Assignments
Lecture Video
Miro Notes
Module 5
05
Compress and Isolate

Techniques for optimizing context windows and isolating critical information for LLMs.
Essential for managing token costs and model performance.

Core Topics

Context Compression
Isolation Strategies
Optimization

Resources & Assignments
Lecture Video
Miro Notes
Colab
Module 6
06
Memory Architectures

Implementing long-term memory and state management for persistent AI agents.
Moving beyond stateless LLM interactions.

Core Topics

Memory Management
Stateless vs Stateful
Architecture

Resources & Assignments
Lecture Video
Miro Notes
Colab
Module 7
07
Workflow Automation

Setting up professional development workflows for building AI agents.
Focuses on OpenClaw automation.

Core Topics

Automation
Workflow Design
OpenClaw

Resources & Assignments
Lecture Video
Miro Notes
Module 8-10
08
Mega Build Projects

Hands-on project phase with industry experts to build and deploy complex AI solutions.
Transition to Phase Two research mentorship.

Core Topics

Project Implementation
Deployment
Agentic Systems