Learn Retrieval Augmented Generation (RAG) Fine-Tuning and LLM Optimization to Build Accurate Real-World AI Applications
Unlock the power of Retrieval Augmented Generation (RAG) and Fine Tuning to build AI systems that are smarter, more accurate, and grounded in real-world data.
What you’ll learn
- Understand the fundamentals of Retrieval Augmented Generation (RAG) and how it enhances the performance of Large Language Models (LLMs)..
- Learn how to fine-tune LLMs to align with domain-specific tasks and improve accuracy, relevance, and reliability..
- Gain hands-on knowledge of how to implement RAG workflows to connect LLMs with real-time, grounded data sources..
- Explore real-world scenarios and use cases where RAG and fine-tuning empower AI to deliver precise, actionable results in enterprise environments..
- Develop the skills to create custom datasets for fine-tuning and train AI models to adapt to specific organizational needs..
- Master techniques to reduce AI hallucination and ensure AI-generated responses are grounded in facts and context..
- Understand how to combine RAG with fine-tuning (RAFT) to create cutting-edge, domain-specific AI solutions..
- Discover the inner workings of LLMs – Understand how large language models generate responses using probabilistic methods and why this can lead to hallucination.
- Learn the importance of context in AI interactions – Explore how providing detailed prompts and context enhances LLM accuracy and relevance..
- Understand embeddings and vector databases – Gain insights into how embeddings help AI interpret queries and retrieve relevant information efficiently..
- Explore knowledge graphs – See how knowledge graphs reduce ambiguity, enhancing AI’s ability to understand relationships between concepts for more accurate resp.
- Implement RAFT (Retrieval-Augmented Fine-Tuning) – Master the combination of RAG and fine-tuning to develop AI systems that can retrieve data and respond accura.
- Recognize enterprise use cases for RAG and fine-tuning – Learn how companies use RAG to power AI chatbots, virtual assistants, and customer service tools that a.
- Design AI solutions that scale – Understand how to implement RAG systems across large organizations, ensuring AI assistants remain up-to-date with evolving data.
Course Content
- Introduction –> 2 lectures • 4min.
- How Large Language Models – LLM’s Work –> 3 lectures • 11min.
- The RAG Flow – Turning LLMs into Precision Tools –> 2 lectures • 8min.
- Advanced Techniques for Structuring and Enriching AI Knowledge –> 1 lecture • 3min.
- Optimizing and Customizing AI Performance –> 2 lectures • 6min.
- Assignment and Quiz –> 0 lectures • 0min.
- Bonus –> 1 lecture • 1min.
Requirements
Unlock the power of Retrieval Augmented Generation (RAG) and Fine Tuning to build AI systems that are smarter, more accurate, and grounded in real-world data.
In this course, you’ll explore how large language models (LLMs) can transform enterprise operations—reducing hallucinations, enhancing accuracy, and personalizing outputs to fit your organization’s unique needs. By mastering RAG, you’ll learn to connect AI to live data sources, allowing it to retrieve and generate precise, up-to-date responses.
Fine-tuning, on the other hand, ensures your AI speaks your language—whether that’s adapting to industry-specific jargon, workflows, or brand voice. Together, RAG and fine-tuning make LLMs not just functional, but indispensable for business.
With real-world examples and hands-on insights, this course will show you how enterprises are deploying these techniques to build next-generation AI tools. By the end, you’ll have the knowledge to design AI that drives efficiency, customer satisfaction, and innovation.
What You’ll Learn:
- Implement RAG to ground LLMs in real-time, domain-specific data.
- Fine-tune LLMs to customize their behavior for enterprise applications.
- Understand embeddings, knowledge graphs, and their role in refining AI outputs.
- Deploy AI workflows that integrate retrieval, augmentation, and generation for accurate, actionable responses.
- Master RAFT (Retrieval-Augmented Fine-Tuning) to build AI models that are both powerful and precise.
Why Take This Course?
- Gain cutting-edge skills in RAG, fine-tuning, and LLM optimization.
- Learn by example with practical scenarios from enterprise AI deployments.
- No advanced programming required – concepts are presented in a clear, accessible format.
- Ideal for AI developers, data scientists, product managers, and business leaders exploring AI adoption.
Who This Course Is For:
- AI developers and engineers wanting to enhance LLM performance with RAG.
- Data scientists focused on improving AI accuracy and grounding.
- Business leaders and managers exploring AI-driven automation and workflows.
- Students and researchers interested in advanced AI techniques and enterprise use cases.