Chatgpt Prompt Engineering Guide - All You Need to Know
by Alaguvelan M | Updated Apr 07, 2023
ChatGpt Prompt Engineering Guide
Autoregression is the technique used by ChatGPT to generate responses by predicting the next word in a sequence based on the preceding words. However, the quality of the answer depends not only on the autoregression technique but also on the quality of the question asked. Several tech companies are introducing generative AI tools based on LLMs for automating business tasks. Recently, Microsoft released a chatbot based on OpenAI's ChatGPT to a select group of users. This chatbot is integrated into Microsoft 365 and has the capability to automate CRM and ERP application functions. Tech companies like Microsoft and Salesforce have introduced generative AI tools based on LLMs, like OpenAI's GPT-4, for business use. For instance, Microsoft 365 Copilot integrated with OpenAI’s ChatGPT can create a first draft of a document, while Salesforce's chatbot will work with its CRM platform. Although LLM-based chatbots can make errors, these pre-trained models produce the most accurate and compelling content that can be used as a starting point. GPT-3 operates on a 175-billion-parameter model that can generate text and code using short prompts. The newer GPT-4 model, estimated to have up to 280 billion parameters, is expected to produce even more accurate responses. Several well-known generative AI platforms, aside from OpenAI's GPT LLM, include Hugging Face's BLOOM and XLM-RoBERTa, Nvidia's NeMO LLM, XLNet, Co: here, and GLM-130B.
Reviewing The Conversation Task
We explored conversation capabilities and role prompting, which involves instructing the LLM to have a conversation in a specific style and with a specific intent and identity. We reviewed an example of a conversational system that generates technical and scientific responses to questions, highlighting the importance of intent and identity. The example worked well with text-DaVinci-003, but OpenAI's ChatGPT APIs, specifically the gpt-3.5-turbo model, offer even more powerful and cost-effective chat completions. Companies like Snap Inc. and Instacart already integrate ChatGPT-powered conversational features into their products, providing personalized recommendations and open-ended shopping goals.
Conversations with ChatGPT
Starting with the same chatbot assistant example mentioned earlier, let's explore the results obtained using ChatGPT. Unlike text-DaVinci-003, ChatGPT's underlying get-3.5-turbo model uses a chat format as input. This means that the model expects a series of messages as input and generates a response based on those messages. However, it is worth noting that the ChatGPT chat completion API requires messages to be in a specific format, which may differ from the example provided earlier. To illustrate, a snapshot of the Chat Mode in the OpenAI Playground is provided below. It is expected that developers will interact with ChatGPT using the Chat Markup Language in the future.
Single-turn tasks
ChatGPT supports both multi-turn conversations and single-turn tasks, just like text-DaVinci-003. By using ChatGPT, we can perform the same tasks as demonstrated with the original GPT models. We can label the conversation with USER and ASSISTANT to illustrate how the task is performed using ChatGPT. The example can be seen using the OpenAI Playground. To make the API call, the Chat Markup Language is used. Using the chat format with ChatGPT opens up new possibilities for conversational AI, allowing for more engaging and dynamic interactions between users and chatbots. With its advanced language modeling capabilities and efficient cost structure, ChatGPT is quickly becoming a popular choice for businesses looking to automate customer service and support tasks.
ChatGpt
ChatGPT is a chatbot powered by artificial intelligence, created by OpenAI, and launched in November 2022. It is built on top of OpenAI's GPT-3.5 and GPT-4 large language models and has been fine-tuned using supervised and reinforcement learning techniques. Despite gaining attention for its articulate answers and detailed responses across various domains of knowledge, ChatGPT has been criticized for its inconsistent factual accuracy. The launch of ChatGPT resulted in OpenAI's valuation being estimated at $29 billion in 2023. ChatGPT's initial release was based on GPT-3.5, but a version based on the newer GPT-4 model was released on March 14, 2023, limited to paid subscribers. ChatGPT, which is a part of the GPT family of language models, was fine-tuned using a more advanced version of GPT-3 known as "GPT-3.5". The fine-tuning process included both supervised and reinforcement learning techniques through a process called reinforcement learning from human feedback (RLHF). Human trainers were involved in both approaches to improve the model's performance. In supervised learning, the model was provided with conversations where the trainers played both user and AI assistant roles. In reinforcement learning, human trainers ranked responses the model had generated in a previous conversation to create "reward models" that the model was further fine-tuned on using several iterations of Proximal Policy Optimization (PPO) algorithms. ChatGPT initially used a Microsoft Azure supercomputing infrastructure powered by Nvidia GPUs, specifically built for OpenAI, costing hundreds of millions of dollars. OpenAI collects user data to improve the model, and users can provide additional feedback by upvoting or downvoting responses and providing comments in a text field.
Prompt Engineering
Prompt engineering is a technique used in natural language processing (NLP) within the field of artificial intelligence (AI). It involves embedding a clear description of the task that the AI needs to perform within the input, often in the form of a question, instead of it being implicitly given. This approach usually involves converting one or more tasks into a prompt-based dataset and training a language model using "prompt-based learning" or "prompt learning." According to Marshall Choy, Senior Vice President of Product at SambaNova Systems, prompt engineering is the process of optimizing text prompts to achieve desired outcomes for large language models. It enables quick and easy alignment of the language model with the task definition, making it ideal for rapid prototyping and exploration. Additionally, Eno Reyes, a machine learning engineer with Hugging Face, notes that prompt engineering is expected to become an important skill for IT and business professionals. Prompt engineering has been greatly advanced by the GPT-2 and GPT-3 language models, which have paved the way for multitask prompt engineering using multiple NLP datasets. This approach has demonstrated good performance on new tasks in 2021. Another method called chain-of-thought (CoT) prompting involves providing few-shot examples of a task to the language model to improve its reasoning abilities. These developments have been made accessible to a wide audience through the publication of several open-source notebooks and community-led projects, including image synthesis. As of February 2022, there were reportedly over 2,000 public prompts available for around 170 datasets.
Chatgpt Prompt Engineering Guide - FAQs
Prompt engineering is the process of crafting and optimizing text prompts for ChatGPT to achieve desired outcomes. It involves embedding a clear description of the task that ChatGPT is supposed to accomplish in the input, such as a question or instruction.
Prompt engineering in ChatGPT works by converting one or more tasks to a prompt-based dataset and training the language model with prompt-based learning. This helps ChatGPT better align with the task definition quickly and easily.
Prompt engineering allows for rapid iteration in product prototyping and exploration, making it easier to tailor ChatGPT to specific tasks. It can also improve accuracy and ensure that ChatGPT provides more relevant and useful responses.
There are many tools and resources available for prompt engineering in ChatGPT, including open-source notebooks, community-led projects, and NLP datasets. Some popular generative AI platforms besides ChatGPT include Hugging Face's BLOOM and XLM-RoBERTa, Nvidia's NeMO LLM, XLNet, Co: here, and GLM-130B.