This lesson on advanced prompting teaches techniques to improve AI responses. It covers prompt engineering—constructing and optimizing prompts—and six techniques: few-shot, chain of thought, generated knowledge, least to most, self-refine, and myopic prompting. These methods guide the AI towards more relevant and accurate results, including deterministic outputs. This segment details the "chain of thought" prompting technique, illustrating how breaking down complex problems into smaller, manageable steps significantly improves the accuracy of Large Language Model (LLM) responses, particularly in mathematical problems. The example of Alice's apples clearly demonstrates how providing the LLM with a similar calculation and step-by-step approach increases the likelihood of a correct answer, highlighting the importance of this technique for ensuring accurate results from LLMs. Improve Prompt Outcomes: Learn prompt engineering techniques to enhance the relevance and quality of AI responses. Control Randomness: Master prompting to achieve either highly random or highly deterministic results (consistent answers). Prompt Engineering Techniques: Utilize techniques like few-shot prompting, chain of thought, generated knowledge, least to most, self-refine, and myopic prompting to optimize prompts. Few-Shot Prompting: The most basic form; create a prompt and receive a response. Chain of Thought: Guide the AI to break down complex problems step-by-step, improving accuracy, especially in calculations. Generated Knowledge: Incorporate your own data (e.g., company information) into prompts to leverage existing knowledge and avoid extensive retraining. Least to Most: Break down problems into parts and specify the order of execution, useful for multi-step processes. Self-Refine: Iteratively improve AI responses by requesting gradual refinements and improvements. Myopic Prompting: Critically evaluate the AI's responses, questioning its answers to identify contradictions and ensure accuracy.
