Summary
Highlights
The video introduces 12 prompting techniques from a Google prompt engineering guide. It highlights the importance of prompt engineering in guiding LLMs to produce accurate outputs by optimizing prompt length, writing style, and structure.
Explains zero-shot prompting, where no examples are provided to the AI. The AI relies solely on its pre-existing knowledge to answer, like asking it to tell a joke without specifying the type or language.
Covers one-shot prompting, where a single example or reference is given to the AI to guide its response. For example, providing a single review and labeling it as negative to influence the sentiment analysis of subsequent reviews.
Explains few-shot prompting, which involves providing multiple examples to the AI to establish a pattern. The AI learns to follow this pattern to generate results, using three to five examples for general cases and more for complex tasks.
Discusses system prompting, which sets the overall context and purpose for the language model. It defines the big picture of what the model should be doing and allows for specifying output formats, like requesting a JSON output or a response in a specific language.
Covers role prompting, where a specific role is assigned to the AI, such as a motivational speaker or a kindergarten teacher. The AI then tailors its responses to align with the assigned role's tone, style, and expertise.
Explains contextual prompting, which involves providing the AI with context about the specific task or situation. This helps ensure seamless and efficient AI interactions by enabling the model to understand requests and generate more accurate and relevant responses.
Introduces step-back prompting, an extension of contextual prompting where the AI is first asked to gather more information about a topic before answering the main question. The information gathered is then used as context in the subsequent prompt.
Covers chain of thought prompting, a technique that improves the reasoning capability of LLMs by generating intermediate reasoning steps. Doing so makes the model provide a more accurate answer.
Explains self-consistency prompting, which involves asking the same prompt multiple times to obtain different answers and selecting the best one. The presenter finds this method frustrating because it relies on repeated attempts to get a better.
Discusses Tree of Thoughts, an approach that generalizes chain of thought prompting by allowing the LLM to explore multiple different reasoning paths. Input yields three different ways of solving the problem, with each step again yielding multiple ways of solving, ultimately improving the answer.
Describes RE Act prompting enabling LLMs to solve complex problems using external tools. It mimics human operation in the real world where a plan is created (reasoning) and then tools are used for certain actions (acting).
Covers automatic prompt engineering, where the AI is asked to write a prompt that can be used to generate a LinkedIn post from a given topic. This automates the prompt writing process.
Briefly mentions code prompting, which involves using prompts to write or explain code. The presenter encourages viewers to explore code-related prompts from the provided book link.