With the democratisation of artificial intelligence and large language models (LLMs), problem solving has been turned on its head. For many problems, it is no longer necessary to create a computer programme using a complex programming language. Instead, all you need is a good text prompt.
More specifically, advanced prompt engineering, which is redefining the way we optimise the performance of language models such as ChatGPT.
Discover the benefits of advanced prompt engineering in the age of AI, with 4 concrete examples of techniques you need to know.
Advanced Prompt Engineering, a sophisticated approach to the use of LLMs
Advanced prompt engineering refers to the process of refining the input of a language model in order to obtain the desired output without modifying the actual model weights. It is a sophisticated approach that goes beyond traditional adjustments of model weights. Here, the focus is on skillfully refining the input to influence the desired output.
For example, to solve certain problems on GPT-3, a simple phrase was added: “Let’s think step by step”. The results are astonishing! The resolution rate for GPT-3 rose from 18% to 79%.
By developing well-structured prompts, you can guide language models to produce accurate and consistent results. But be careful, because to exploit their full potential, it’s not enough to simply ask questions. You need to optimise the inputs, with clear instructions, to influence the behaviour of the model and guarantee relevant results.
4 advanced prompt engineering techniques
Mastering advanced prompt engineering means knowing the different guided message techniques to achieve the desired result. Here are the most common techniques.
Zero shots Prompt
Zero Shots Prompt is one of the first advanced Prompt engineering techniques. It simply involves asking a question or presenting a specific task to the language model. This is done without the model having been specifically pre-trained for the task or question. In other words, it has never been exposed to specific examples relating to this task during its training phase.
This approach is often used to assess the generalisability of a language model. And for good reason, with the Zero shots prompt, it demonstrates that it is capable of responding to queries for which it was not explicitly programmed during its training.
Chain of thought prompting (CoT)
Chain of Thought suggests a sequence of interconnected thoughts or ideas. Applied to machine learning, CoT improves the model’s performance on more complex reasoning tasks.
In concrete terms, the engineers behind this advanced prompt engineering technique have studied the generation of a human chain of thought. This involves a series of intermediate reasoning stages.
To apply these principles to language models, they need to be given a few demonstrations of chains of thought as examples. Then ask them to produce a few steps of reasoning before providing the final answer. This enables them to carry out complex reasoning.
Self Consistency
In science and engineering, self-consistency means that the different parts or components of a system interact in such a way as to maintain their internal coherence.
In machine learning, self consistency is an improved approach to CoT for solving even more complex reasoning problems.
Using this advanced prompt engineering technique, reasoning paths are sampled. This allows the language model to select the most consistent answer (without taking into account only the fastest path).
This prompting technique is particularly well suited to complex reasoning problems where several modes of thought exist to lead to a correct answer.
ReAct
ReAct is an advanced prompt engineering technique developed in 2022. It is a synergy between Reasoning and Acting.
This method involves LLMs generating both reasoning and task-specific actions. Reasoning helps the model to generate an action plan. Actions, on the other hand, facilitate access to external sources of knowledge. This makes it possible to gather additional information, and thus improve LLM reasoning.
With ReAct, reasoning and action are intimately intertwined to further improve the results of the language model.
Mastering advanced prompt engineering with DataScientest
At a time when solutions based on generative AI are multiplying, mastery of advanced prompt engineering is proving indispensable. Particularly for data science experts. To improve your language models, you will need to use advanced prompt engineering. But that’s not all. There are other techniques you need to know about to create high-performance LLMs. That’s why it’s a good idea to get trained with DataScientest.
