Course Outline
In this course, we’ll explore how to increase and enrich context beyond an LLM’s native context window using advanced techniques like Retrieval-Augmented Generation (RAG), Knowledge Graphs, and Contextual Memory Systems. You'll learn how these methods bring in external knowledge, structure relationships between data points, and preserve session-based memory—allowing AI models to generate more accurate, coherent, and deeply informed responses. By the end, you’ll be equipped to build AI systems that leverage enriched context for enhanced reasoning and decision-making.
Learning Outcomes
- Understand the limitations of LLM context windows and how to extend them
- Learn how to implement Retrieval-Augmented Generation (RAG) for dynamic knowledge retrieval
- Explore Knowledge Graphs to structure and relate information meaningfully
- Utilize Contextual Memory Systems for personalized and session-aware AI interactions
Who Is This Course For?
This course is designed for AI developers, ML engineers, and product teams looking to enhance LLM responses by incorporating context-rich methodologies. Whether you’re working on intelligent chatbots, AI-driven research assistants, or domain-specific knowledge applications, this course will give you the tools to build AI systems that retain and reason with enriched data.
Why Enroll?
Going beyond an LLM’s context window is key to building smarter, more capable AI applications. By enrolling, you’ll gain practical strategies to extend your model’s memory and structure information efficiently, enabling better accuracy and relevance in AI-driven interactions.
Pre-requisites
- Familiarity with LLMs and prompt engineering
- Basic knowledge of Python and API integrations
- Interest in advanced AI workflows and memory systems
Let’s Get Started!
Ready to take LLM capabilities to the next level? Enroll now and start engineering AI systems with enriched context!
