Prompt Engineering Basics: ReAct Framework, Tool Calling, and Agent Design Explained

A tutorial session on prompt engineering covered the ReAct (Reasoning and Action) framework, explaining how large language models (LLMs) function as pre-trained model files that generate responses based on existing data. When an LLM cannot answer a query on its own, it relies on external tools — essentially scripts or programs — to fetch or process additional information. The concept of an Agent was introduced as the orchestration layer that combines an LLM with tools, with LangChain highlighted as a stable framework for building such agents. Several prompting patterns were discussed, including RICE (Role, Input, Constraints, Expectations) and CRISP (Context, Role, Instruction, Style, Purpose), alongside techniques like zero-shot, few-shot, chain-of-thought, and tree-of-thought prompting. Groq was noted as the cloud infrastructure provider used to run large models such as LLaMA 3 and GPT-OSS 120B, since running them locally is not feasible.