LARGE LANGUAGE MODELS - Prompt Engineering

Advanced Techniques.

Advanced prompt engineering techniques involve using prompt patterns to tap into powerful capabilities within LLMs. Prompt patterns are pre-defined templates that can be used to generate specific types of outputs from LLMs. They can be used to generate text in different styles, summarize long texts into shorter ones, or even generate code snippets based on natural language prompts.

For example, GPT-3(now 4) developed by OpenAI has a built-in feature called “inference API” that allows users to create custom prompt patterns for generating specific types of outputs. Another example is DALL-E developed by OpenAI, which has a built-in feature called “image generation API” that allows users to generate images from textual descriptions.

Prompt engineering can also involve using “control codes” to modify the behavior of LLMs. Control codes are special tokens that can be inserted into prompts to modify the output of LLMs. They can be used to control the style, tone, or content of generated text.

Another advanced prompt engineering technique is “prompt tuning”, which involves fine-tuning LLMs on specific prompts to improve their performance. Prompt tuning can be used to improve the accuracy and relevance of generated text.

Advanced prompt engineering techniques involve using prompt patterns, control codes, and prompt tuning to tap into powerful capabilities within LLMs. These techniques can be used to generate specific types of outputs from LLMs and improve their performance.

In addition to using prompt patterns and control codes, there are several other advanced prompt engineering techniques that can be used to generate high-quality and relevant outputs from LLMs.

One such technique is few-shot learning, which involves training LLMs on a small number of examples or demonstrations to improve their performance on various tasks. Few-shot learning can be used to improve the accuracy and relevance of generated text.

Another advanced prompt engineering technique is chain-of-thought prompting, which involves generating text in a conversational manner. Chain-of-thought prompting can be used to generate more engaging and interactive outputs from LLMs.

Self-consistency is another advanced prompt engineering technique that involves generating text that is consistent with previous outputs. Self-consistency can be used to generate more coherent and fluent text from LLMs.

Knowledge generation prompting is another advanced prompt engineering technique that involves generating text based on a given set of facts or knowledge. Knowledge generation prompting can be used to generate more informative and accurate text from LLMs.

ReAct is an advanced prompt engineering technique that involves generating text based on user feedback. ReAct can be used to generate more personalized and relevant outputs from LLMs.

Reward Prompting.  Reward prompting provides the LLM with a goal seeking reward/punishment behavioural motivator/modification and is most useful in a conversational context.  Its purpose is to reward (or punish) the LLM for adhering (or failure to adhere) to a set of guidelines included in the prompt.  

These are just some examples of the many advanced prompt engineering techniques that can be used to generate high-quality and relevant outputs from LLMs.

...Control Codes for Prompt Engineering....