Prompt Engineering 101 - The Complete Beginner’s Guide

Discover how prompt engineering shapes AI behavior by defining clear task framing, context, and output constraints. Improve input quality to reduce ambiguity, hallucinations, and unreliable outputs in real applications.

Identify vague prompts and transform them into specific, powerful instructions that define how, why, and what you want. Guide AI with clear context and expectations to dramatically improve output quality.

Explore the anatomy of a good prompt in prompt engineering by dissecting instruction, context, input type, and output type, and learn how each component reduces uncertainty and improves output.

Explore core prompting techniques—zero-shot, few-shot, and role-based prompts—through hands-on exercises in Google Colab, experimenting with OpenAI, Google, Gemini, and Anthropic outputs.

Investigate chain of thought and structured guidance to improve model reasoning. Apply guided reasoning for decision making, planning, comparisons, debugging, and reducing hallucinations.

Master context engineering to improve large language model responses by selecting and organizing relevant information within the context window. Learn how context shapes outputs and avoids context rot.

Learn how to manage large context with summarization, context slicing, and progressive prompting to keep focus and reduce noise by sharing only relevant signals.

Learn to separate permanent instructions in system prompts from temporary instructions in user prompts, applying a two-layer approach that defines identity and rules while handling current tasks with clear priority.

Explore multiple reasoning paths with the tree of thoughts, generating and evaluating options before expanding the strongest ones. Move beyond linear thinking, discard weak thoughts, and reach a solid conclusion.

Master the React prompting technique, a reasoning plus acting loop that reasons, acts with tools or APIs, observes outcomes, and adapts dynamically.

Compare zero-shot, few-shot, role-based, chain-of-thought, system and user prompts, tree of thoughts, dot, and react prompting; explain when to use each and avoid common mistakes.