Whether you're a developer integrating into your software or a no-coder, marketer, or business analyst adopting AI, prompt engineering is a MUST-HAVE skill that we need to acquire. AI In the following video, Andrej Karpathy, one of the well-known prominent figures in AI, gives a golden lesson on prompt engineering: https://youtu.be/bZQun8Y4L2A?si=jg6Da4jT05Nn2VbE&embedable=true We all use like ChatGPT, Claude, or Llama to generate human-like text and assist in a wide range of tasks, from answering questions to generating creative content. LLMs However, to effectively use these models, it is crucial to understand the process of training them and how to prompt them to achieve the desired results. In this post, I will give you various techniques to harness the full potential of large language models that I have learned from Andrej’s speech. Training Large Language Models The training process of large language models like GPT involves several stages;
1- Pre-training
2- Supervised fine-tuning
3- Reward modeling
4- Reinforcement learning. is the initial stage where the model is trained on a vast amount of data, including web scrapes, and high-quality datasets like HuggingFace, Github, Wikipedia, books, and more. The data is preprocessed to convert it into a suitable format for training the neural network. Pre-training Pre-training, the model predicts the next token in a sequence. This process is repeated for numerous tokens, enabling the model to learn the underlying patterns and structures of the language. The resulting model has billions of parameters (1T GPT-4, 176B GPT-3, 130B Claude-2, 7B, 13B, and 70B Llama-2), making it a powerful tool for various tasks. is the next stage, where the model is trained on specific datasets with labeled examples. Humans behind computers gather data in the form of prompts and ideal responses, creating a training set for the model. The model is trained to generate appropriate responses based on the given prompts. This fine-tuning process helps the model specialize in specific tasks. Supervised fine-tuning are additional stages that can be applied to further improve the model's performance. In reward modeling, the model is trained to predict the quality of different completions for a given prompt. This allows the model to learn which completions are more desirable and helps in generating high-quality responses. Reinforcement learning involves training the model with respect to a reward model, refining its language generation capabilities. Reward modeling and reinforcement learning Effective Prompt Engineering Techniques Prompt engineering plays a crucial role in effectively utilizing large language models. Here are some techniques that can enhance the performance and control the output of these models: When prompting the model, ensure that the prompts are task-relevant and include clear instructions. Think about how those humans behind the computer contractor would approach the task and provide prompts accordingly. Including relevant instructions helps guide the model's response. Task-Relevant Prompts: Incorporate relevant context and information into the prompts. By retrieving and adding context from external sources, such as documents or databases, you can enhance the model's understanding and generate more accurate responses. This technique allows the model to leverage external knowledge effectively. Retrieval-Augmented Generation: Provide a few examples of the desired output to guide the model's response. By showing the model a few examples of the expected output, you can help it understand the desired format and generate more accurate responses. This technique is particularly useful when dealing with specific formats or templates. Few-Shot Learning: System 2 thinking involves deliberate planning and reasoning. Break down complex tasks into smaller steps and prompt the model accordingly. This approach helps the model to reason step-by-step and generate more accurate and coherent responses. System 2 Thinking: Use constraint prompting to enforce specific templates or formats in the model's output. By reducing the probabilities of specific tokens, you can guide the model to fill in the blanks according to the desired format. This technique ensures that the model adheres to specific constraints while generating responses. Constraint Prompting: Fine-tuning the model can further enhance its performance for specific tasks. By training the model on task-specific datasets, you can specialize its language generation capabilities. However, fine-tuning requires careful consideration and expertise, as it involves complex data pipelines and may slow down the training process. Fine-Tuning: Finally, it is important to consider the limitations and potential biases of these models. They may generate false information, make reasoning errors, or be susceptible to various attacks. Therefore, it is advisable to use them with human oversight and treat them as sources of suggestions rather than completely autonomous systems. Prompt engineering is a crucial aspect of effectively utilizing large language models. Andrej's Microsoft Developer Conference video is a great source for understanding the overall sight of LLMs. Also published here.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Prompt Engineering: Understanding the Potential of Large Language Models

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