Mastering Large Language Models 2026 Frontier & Open-Source Focus Training Course
Master the latest Large Language Models (LLMs) in 2026 with Educad Academy’s hands-on training in frontier AI and open-source technologies. This course covers state-of-the-art models including GPT-5, Claude 4, Gemini 2.5 Pro, Llama 4, Qwen3, Grok 4, and DeepSeek-R1. Learn contemporary training paradigms like RLHF, DPO, and MoE architectures, benchmark model performance across reasoning, coding, math, long-context, and multimodal tasks, and master model-specific prompt engineering. Gain expertise in efficient fine-tuning with LoRA, build production-grade RAG systems, implement AI safety and alignment protocols, and explore emerging trends toward AGI. Perfect for AI engineers, data scientists, and developers aiming to stay ahead in advanced LLM research and deployment.
Course Objectives:
- Explore state-of-the-art LLMs and their capabilities.
- Understand modern training paradigms, including RLHF, DPO, synthetic data, and MoE architectures.
- Benchmark LLM performance across reasoning, coding, math, long-context, and multimodal tasks.
- Master optimized prompting techniques for closed and open-source models.
- Learn efficient fine-tuning strategies and LoRA customization.
- Build production-ready Retrieval-Augmented Generation (RAG) pipelines.
- Apply safety, alignment, and red-teaming protocols for responsible AI.
- Analyze emerging trends in AI toward AGI.
Course Content:
Module 1: State-of-the-Art LLMs Overview
- Deep dive into GPT-5, Claude 4, Gemini 2.5 Pro, Grok 4, Llama 4, Qwen3, DeepSeek-R1 & more.
Module 2: Contemporary Training Paradigms
- Understand post-training, RLHF, DPO, synthetic data, and MoE architectures used today.
Module 3: Capability Benchmarking 2026
- Compare reasoning, coding, math, long-context, and multimodal performance.
Module 4: Model-Specific Prompting Mastery
- Write optimized prompts for frontier closed and top open-source models.
Module 5: Efficient Fine-Tuning & LoRA
- Customize Llama-4, Gemma-2-27B, Qwen3-72B, Kimi-Dev-72B, and similar models cost-effectively.
Module 6: Production-Grade RAG Systems
- Build accurate, up-to-date retrieval pipelines with 2026 best practices.
Module 7: Safety, Alignment & Red-Teaming
- Apply latest constitutional AI, circuit breakers, and moderation layers.
Module 8: The Road to AGI
- Analyze emerging trends: agents, self-improvement, open-weight scaling, multimodal unification.
Learning Outcomes:
By the end of this course, learners will be able to:
- Evaluate and compare advanced LLMs on multiple capabilities.
- Craft highly effective prompts for diverse models.
- Fine-tune open-source and proprietary LLMs efficiently.
- Design and deploy production-grade RAG systems.
- Implement AI safety, alignment, and moderation strategies.
- Understand the emerging trends and roadmap toward AGI.
Target Audience:
- AI researchers, engineers, and developers.
- Data scientists and ML practitioners seeking advanced LLM expertise.
- Professionals in AI-driven product development or enterprise AI adoption.
- Students and enthusiasts aiming to work with frontier and open-source LLMs.
Course Prerequisites:
- Basic understanding of machine learning and neural networks.
- Familiarity with Python and common ML libraries (PyTorch, TensorFlow).
- Optional: prior experience with LLMs or NLP frameworks is helpful but not required.
International Student Fee: 1000 USD
Flexible Class Options
- Corporate Group Training | Fast-Track
- Weekend Classes For Professionals SAT | SUN
- Online Classes-Live Virtual Class (L.V.C) Online Training
