Advanced LLM Architectures and Fine-Tuning for Product Developers

Unlock the next frontier of AI product development by mastering cutting-edge LLM architectures and advanced fine-tuning techniques for unparalleled performance and innovation.

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Beyond Standard Transformers: Advanced LLM Architectures

Unit 1: Why Go Beyond Standard Transformers?

Unit 2: Mixture-of-Experts (MoE) Models

Unit 3: Retrieval-Augmented Generation (RAG)

Unit 4: Comparing & Combining Architectures

Foundational LLM Fine-Tuning Methodologies

Unit 1: Fine-Tuning Paradigms: An Overview

Unit 2: Mastering Full Fine-Tuning

Unit 3: Prompt Engineering & Tuning

Unit 4: Choosing the Right Methodology

Parameter-Efficient Fine-Tuning (PEFT) Techniques

Unit 1: Why PEFT? The Need for Efficiency

Unit 2: Deep Dive into LoRA

Unit 3: QLoRA and Beyond

Unit 4: Choosing the Right PEFT

Optimizing LLM Fine-Tuning Workflows

Unit 1: Data Preparation for Fine-Tuning

Unit 2: Hyperparameter Tuning Strategies

Unit 3: Efficient Resource Management

Unit 4: Troubleshooting and Debugging

Evaluating and Aligning Fine-Tuned LLMs

Unit 1: Quantitative Evaluation of LLMs

Unit 2: Qualitative Evaluation & Human Feedback

Unit 3: Bias Mitigation in LLMs

Unit 4: LLM Alignment and Ethics

Emerging Trends and Future of Generative AI

Unit 1: Multi-Modal LLMs: Beyond Text

Unit 2: Agentic Systems and Self-Correction

Unit 3: Future of Generative AI