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It’s a common jest in tech circles to dismiss Large Language Models (LLMs) as glorified “Spicy Autocompletes”. Such quips, though amusing, spring from a shallow grasp of the technology. True, the earlier GPT-2 was primarily trained to predict the next token in a sequence. However, its successors, GPT-3 and GPT-4, represent leaps in development, employing diverse techniques like Reinforcement Learning with Human Feedback (RLHF) in their training.
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In fact, there’s compelling evidence that Large Language Models possess the capability to engage in logical reasoning. The realm of Prompt Engineering has been a game-changer here. Techniques like Chain of Thought prompting and Thinking Step-by-Step have shown to dramatically elevate a model’s performance, all without the need for further training cycles.
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My goal was to put this to the test. This article will show the effects of thinking step-by-step for a highly complex reasoning task.
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Large Language Models are Zero Shot Reasoners
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Models like GPT 3.5 excel in text generation and summarization tasks, but they often face challenges in areas requiring complex reasoning, such as coding and mathematics, where achieving accurate results can be more difficult.
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Clever prompt engineering can significantly mitigate this issue. By instructing the model to engage in a thought process before delivering its response, the model’s performance can see a substantial improvement.
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After reading about the potential of enhanced prompt engineering, I decided to apply this concept to my AI–Powered Trading Platform NexusTrade. NexusTrade boasts an advanced AI-Powered Chat capable of executing a range of intricate tasks, notably the generation of highly customizable automated trading strategies. My objective was to enhance the AI Chat’s capabilities without resorting to GPT-4, which is 10 times more expensive than GPT-3.5.
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An Example of My Original Prompts
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In shaping the AI Chat Application for NexusTrade, I utilized NexusGenAI, our dedicated configuration platform. NexusGenAI significantly simplifies the creation and maintenance of AI applications. It allows for the storage of various prompts, complete with schemas and examples, enabling the development of complex AI applications in a No-Code/Low-Code environment. This platform streamlines the testing and updating process of AI applications, making it effortlessly manageable.
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The instructions provided in the original prompt were straightforward, focusing primarily on the task at hand. The prompt was designed to be clear and concise, detailing the specific task required from the AI. To aid in understanding and execution, numerous examples were included, offering a comprehensive perspective on the task and ensuring the AI had sufficient context to perform effectively.
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The model demonstrated proficiency in creating basic trading strategies, yet it faced challenges when tasked with more complex strategies. Consider the following example for illustration.
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My goal involved creating a highly intricate technical indicator, a task demanding significant reasoning skills to translate plain English into a tradable strategy compatible with NexusTrade. The strategy included a complex indicator as follows:
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SPY’s price / SPY’s SD + QQQ’s price / QQQ’s SD
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The model’s output for this was presented in a JSON format:
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To the uninitiated, this output might seem correct. However, a detailed examination reveals that the model actually produced a different technical indicator:
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SPY’s Price / SPY’s SD / QQQ’s Price / QQQ’s SD
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The question then arises: how can the model’s output be refined and improved?
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An Example of My Updated Prompts
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In addressing this issue, I revamped my prompts to incorporate the “thinking step-by-step” approach. However, I took it a notch above what the original research paper suggested. Rather than limiting this method to zero-shot reasoning, I integrated step-by-step thinking into all of my examples.
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In a clever move, I fed my conversation directly to GPT-4 and instructed it to process the information step-by-step.
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Remarkably, it took mere MINUTES to test and implement these updates in my application. For a chat application as complex as mine, a typical development team might spend hours or even days testing, finalizing, and deploying such significant code changes. But with NexusGenAI, this process was significantly streamlined.
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Thanks to this approach, I could seamlessly integrate reasoning capabilities and immediately observe the tangible improvements in my models.
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The Effect on NexusTrade – Creating Powerful Reasoning Agents
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The transformation this shift brought to NexusTrade was nothing short of astonishing. The impact of the revised prompt engineering approach was immediately evident in the quality and accuracy of the outputs.
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This nuanced adjustment in how the prompts were structured empowered the model to effortlessly create highly complex trading indicators, previously thought to be beyond its scope. The sophistication of these indicators was such that they could easily rival those generated by much more advanced and expensive models like GPT-4. This advancement was achieved without incurring significant additional costs, showcasing the efficiency and effectiveness of the updated prompt engineering strategy. The ability to generate complex indicators with such precision and cost-effectiveness marked a significant leap forward for NexusTrade, highlighting its potential to become a leading tool in the realm of AI-powered trading platforms.
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Conclusion: Revolutionizing AI with Strategic Prompt Engineering
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The journey from dismissing Large Language Models as mere “Spicy Autocompletes” to harnessing their full potential through innovative prompt engineering culminates in a remarkable transformation, as exemplified by NexusTrade. This platform, enhanced by NexusGenAI, stands as a testament to the power of refined prompt engineering techniques. The improvements in NexusTrade’s AI-powered chat, capable of generating complex trading strategies with impressive accuracy and efficiency, underscore the vast untapped potential of LLMs beyond basic text generation tasks.
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The implementation of step-by-step reasoning and Chain of Thought techniques not only rebuts the initial skepticism surrounding LLMs but also opens new avenues for their application in complex reasoning tasks. The success of these methods in NexusTrade demonstrates that with thoughtful and strategic prompt design, even smaller models can achieve results comparable to their more advanced counterparts like GPT-4, but at a fraction of the cost. This not only enhances the viability of AI applications in various sectors but also encourages continued exploration and experimentation in prompt engineering, paving the way for even more groundbreaking uses of AI technology.
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Thank you for reading! Stay tuned for more updates on the NexusTrade platform. Interested in applying AI to finance? Subscribe to Aurora’s Insights! Want to try create your own the AI-Chat application? Sign up for NexusGenAI today!
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