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يحوّل المستوى النهائي المعرفة السابقة إلى القدرة على تصميم وبناء تطبيقات ذكاء اصطناعي حديثة، مع التركيز على LLMs وEmbeddings وRAG وAgents والتقييم والنشر.

المحاور التدريبية

النماذج الحديثة

مقدمة عملية في Neural Networks.
Embeddings وتمثيل البيانات دلاليًا.
Transformers وLLMs والسياق والرموز.

بناء تطبيقات LLM

التعامل مع LLM APIs.
Structured Outputs وTool Use.
إدارة السياق والتدفقات متعددة الخطوات.

RAG واسترجاع المعرفة

Chunking وEmbeddings.
Vector Databases وSemantic Search.
تصميم RAG Pipeline وربطه بمصادر المعرفة.

Agents والجودة

AI Agents واستخدام الأدوات.
Workflows متعددة الخطوات.
Evaluation وGuardrails والسلامة والموثوقية.
أساسيات Deployment وMonitoring.

التطبيق العملي

بناء مكونات AI وربط النماذج بالبيانات والأدوات والواجهات، مع اختبار النتائج وتحسينها.

المشروع الختامي Capstone

تصميم تطبيق AI متكامل مثل مساعد معرفي أو نظام سؤال وجواب على المستندات، يتضمن LLM وRAG والتقييم والتوثيق والعرض النهائي.

المخرجات المتوقعة

بناء تطبيقات تعتمد على LLM APIs.
تصميم RAG وربطه بمصادر المعرفة.
استخدام Embeddings وVector Search.
تقييم جودة ومأمونية أنظمة AI.
تقديم مشروع AI متكامل قابل للتطوير.

المتطلبات السابقة

اجتياز المستويين الأول والثاني شرط إلزامي.
لا يسمح بالدخول المباشر إلى المستوى الثالث.
يتطلب إكمال المسار اجتياز المشروع الختامي والتقييمات.

20:T77e,

The final level develops the ability to design and build modern AI applications using LLMs, embeddings, RAG, agents, evaluation, and deployment concepts.

Training Modules

Modern AI Models

Practical neural network foundations.
Embeddings and semantic representations.
Transformers, LLMs, tokens, and context.

Building LLM Applications

Working with LLM APIs.
Structured outputs and tool use.
Context management and multi-step workflows.

RAG and Knowledge Retrieval

Chunking and embeddings.
Vector databases and semantic search.
Designing RAG pipelines connected to knowledge sources.

Agents and Quality

AI agents and tool use.
Multi-step workflows and orchestration.
Evaluation, guardrails, safety, and reliability.
Deployment and monitoring fundamentals.

Practical Work

Build functional AI components and connect models with data, tools, and interfaces while evaluating and improving results.

Capstone Project

Design an integrated AI application such as a knowledge assistant or document Q&A system using LLMs, RAG, evaluation, documentation, and a final presentation.

Expected Outcomes

Build applications using LLM APIs.
Design RAG systems connected to knowledge sources.
Use embeddings and vector search effectively.
Evaluate AI system quality and safety.
Deliver an integrated AI project.

Prerequisites

Successful completion of Levels 1 and 2 is mandatory.
Direct entry to Level 3 is not permitted.
Completion requires passing the assessments and capstone project.

21:T9b2,

المحاور التدريبية

النماذج الحديثة

مقدمة عملية في Neural Networks.
Embeddings وتمثيل البيانات دلاليًا.
Transformers وLLMs والسياق والرموز.

بناء تطبيقات LLM

التعامل مع LLM APIs.
Structured Outputs وTool Use.
إدارة السياق والتدفقات متعددة الخطوات.

RAG واسترجاع المعرفة

Chunking وEmbeddings.
Vector Databases وSemantic Search.
تصميم RAG Pipeline وربطه بمصادر المعرفة.

Agents والجودة

AI Agents واستخدام الأدوات.
Workflows متعددة الخطوات.
Evaluation وGuardrails والسلامة والموثوقية.
أساسيات Deployment وMonitoring.

التطبيق العملي

بناء مكونات AI وربط النماذج بالبيانات والأدوات والواجهات، مع اختبار النتائج وتحسينها.

المشروع الختامي Capstone

المخرجات المتوقعة

بناء تطبيقات تعتمد على LLM APIs.
تصميم RAG وربطه بمصادر المعرفة.
استخدام Embeddings وVector Search.
تقييم جودة ومأمونية أنظمة AI.
تقديم مشروع AI متكامل قابل للتطوير.

المتطلبات السابقة

اجتياز المستويين الأول والثاني شرط إلزامي.
لا يسمح بالدخول المباشر إلى المستوى الثالث.
يتطلب إكمال المسار اجتياز المشروع الختامي والتقييمات.

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