LLMs: what are they and what opportunities do they offer?
What are the available tools and techniques for effective prompt engineering? What are the strengths and weaknesses of ChatGPT today? How can we help it improve? Anywhere Club Product Manager Leonid Ardaev answers these timely questions and shares useful resources for learning prompt engineering.
What is an LLM or Large Language Model?
— LLM refers to Large Language Models, which are machine learning models that operate with a vast number of parameters. They utilize extensive data and transformer networks to construct their structure. You're probably familiar with models such as GPT-3, GPT-3.5, and, of course, GPT-4, which is used in OpenAI's chatbots. Currently, OpenAI does not disclose specific details about the volume of information and parameters used in GPT-4, but there are reasons to believe it is one of the largest models available.
There are also other less popular models that, however, fall behind OpenAI in many tests. AI21 Labs, an Israeli company, offers Jurassic 1 and Jurassic 2 models. Additionally, Google Bard is a chatbot based on the PaLM-2 model, which recently became widely accessible.
There are several interesting developments from NVIDIA (NeMo, Picasso, and BioNeMo) that are designed for a wide range of applications, from text and video generation to scientific research. Their next version will probably have the largest number of parameters, allowing it to consider more details when generating content.
The variety of language models is great, and each model has its own characteristics, advantages, and specific areas of application.
— GPT (Generative Pre-trained Transformer) is a large language model (LLM), and ChatGPT is based on the GPT model designed for engaging in natural language conversations with humans. ChatGPT can maintain dialogues by remembering previous statements and providing responses that resemble coherent human-like conversations. The model is trained on large volumes of textual data and utilizes transformer architecture for generating responses.
Strengths of ChatGPT
Weaknesses of ChatGPT
Of course, ChatGPT has many more restrictions at this point, and when they will be removed is a matter of time. For now, you need to remember the limitations and try to work around them.
Basic rules for compiling prompts
— Prompt Engineering is an important aspect of interacting with the ChatGPT model. To get an accurate and correct answer, you need to carefully compose a prompt. A prompt for a model cannot be the same as for a human. A person can interpret non-verbal signals and comprehend implicit intentions. The model does not have this ability. It requires sufficient context and a clear understanding of the task. If the model does not receive this information, it will still return a response, but most likely the response will be one that does not match the user's expectations and request.
Tools and techniques for effective prompt engineering
Using tools helps you test and tweak your prompts to find the best options and obtain the results you want.
Useful resources for learning Prompt engineering
— There are already many additional resources that you can use to learn more about Prompt engineering and apply it in practice:
1. HRGPT and Prompt engineering courses can be found on the LinkedIn Learning platform. They provide a professional and more in-depth explanation of these concepts. Examples include:
2. The Learn Prompting resource provides useful resources for learning more about the prompting process.
3. Various IDEs (Integrated Development Environments) offer rich features and are constantly being updated, providing useful context and tools for the efficient creation of prompts. They also integrate with various programming languages and frameworks to solve specific problems.