Ai+ Training

People and companies in 2024 aim to build ever more complex and performant LLM applications. Leveraging context (e.g., Retrieval Augmented Generation, or RAG), reasoning, and access to external tools or functions (Agents), are front and center. For applications to leverage context well, they must provide useful input to the context window (e.g., [in-context learning](https://openai.com/index/language-models-are-few-shot-learners/)), through direct prompting (Prompt Engineering) or search and retrieval (RAG). To leverage reasoning is to leverage the Reasoning-Action ([ReAct](https://arxiv.org/abs/2210.03629)) pattern, and to be “agentic” or “agent-like.” Another way to think about agents is that they enhance search and retrieval through the intelligent use of tools or services.