What topics are covered in the bootcamp curriculum?

• A gentle introduction to foundation LLMs, vector databases, vector embeddings, semantic search and orchestration frameworks
• Difference between fine-tuning and RAG (Retrieval Augmented Generation)
• Common design patterns for building an LLM application on enterprise data
• Processing of single query/task/inference task in in-context learning
• Role of orchestration frameworks like LangChain in overcoming the context window constraint
• Understand the use cases and limitations of LLM agents
• Role of embeddings and vector databases in semantic retrieval
• The need for a semantic cache when building LLM applications at scale
• Trade-offs, challenges and pitfalls faced while building these applications to solve real-world problems