Prompt Engineering: Business Potentials of Chain of Thought Prompting
Over the past few years, we’ve witnessed the exponential rise of AI, especially in reshaping business and product development. A particularly intriguing evolution in this space is the “chain of thought prompting.” As we stand at the intersection of technology and strategy, understanding this concept is pivotal.
NLP has come a long way. From simple keyword searches to the birth of Siri and Alexa, we’ve been pushing towards more conversational AI. The emergence of Large Language Models like Claude, Llama, GPT-4 has only accelerated this journey, enabling machines to understand and generate human-like text.
So what is Chain of Thought Prompting:
At its core, chain of thought prompting is about making AI more dynamic. Traditional prompting might involve asking an AI a question and getting a single response. With chain of thought prompting, the AI can guide a conversation, building upon previous responses to provide deeper insights.
The paper, titled “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” presents a simple yet effective method for improving the performance of language models on reasoning tasks. The authors propose a new approach called chain-of-thought prompting, which involves providing the model with a series of intermediate reasoning steps as exemplars in prompting. This approach allows the model to generate a chain of thought that leads to the final answer, mimicking the way humans solve complex problems.
Here are some of the key takeaways from the paper:
- Chain-of-thought prompting is an emergent ability of model scale. That is, chain-of-thought prompting does not positively impact performance for small models, and only yields performance gains when used with models of ∼100B parameters.
- Chain-of-thought prompting has larger performance gains for more-complicated problems.
- Chain-of-thought prompting via GPT-3 175B and PaLM 540B compares favorably to prior state of the art, which typically finetunes a task-specific model on a labeled training dataset.
- Robustness to annotators, independently-written chains of thought, different exemplars, and various language models.
- Chain-of-thought prompting can also improve performance on tasks requiring a range of commonsense reasoning abilities.
- Chain-of-thought prompting is a promising new method that has the potential to significantly improve the performance of large language models on reasoning tasks.
I find the results of this paper particularly exciting because they demonstrate the potential of chain-of-thought prompting to improve the reasoning abilities of language models. By providing models with a series of intermediate reasoning steps, we can enable them to generate coherent chains of thought that lead to the final answer. This approach has far-reaching implications, as it could potentially be applied to a wide range of tasks that require complex reasoning, such as natural language inference, question answering, and text summarisation.
Example Use Case:
Imagine a business analyst querying a financial model. Instead of just returning numbers, the AI can now guide the analyst through a narrative, explaining trends, anomalies, and potential causes.
Here’s how it works:
The prompt:
The prompt is a set of inputs that define the task the model needs to perform. For our business analyst, the prompt might be a query like, “What caused the dip in sales in the third quarter?”
The chain of thought:
The chain of thought consists of the intermediate reasoning steps that lead to the final answer. For our sales dip example, the chain of thought might unfold as follows:
Step 1: The third quarter sales data is retrieved.
Step 2: Data from the previous quarters is compared to identify the exact difference.
Step 3: External events, market changes, or internal changes during the third quarter are analysed.
Step 4: Identified that a primary product faced supply chain issues in the third quarter.
Step 5: Realized that a major marketing campaign was launched for this product just before the third quarter, but due to supply chain issues, demand was not met.
The prompting:
Prompting is the process of providing the model with the chain of thought as exemplars. The model is given sets of input-output pairs where the input is a business query (like the sales dip question) and the output is the corresponding chain of thought.
The training:
In the context of LLMs like GPT-3 and GPT-4, they are initially trained on vast amounts of text from diverse sources. This foundational training, called pre-training, helps the model recognize language patterns and structures. Now, considering our example use case of a business analyst, if we wanted the model to specifically excel in financial analysis, we could further fine-tune it on financial datasets, making it more adept at such queries. However, with the advanced capabilities of LLMs, fine-tuning might not always be necessary. By providing a few examples in the prompt (few-shot learning) or even without specific examples (zero-shot learning), the model can generate relevant outputs based on its extensive pre-training.
The evaluation:
After any potential fine-tuning, the model is assessed using a set of new business queries to evaluate its capability in generating coherent and accurate chains of thought.
Business Implications of Advanced Prompting
Incorporating chain of thought prompting can transform user experiences. Applications become more interactive, and interfaces more intuitive. For businesses, this means:
- Enhanced Customer Engagement: E-commerce platforms can guide customers through product selections based on preferences, past purchases, and real-time feedback.
- Efficient Decision-making: Managers can interact with data visualization tools that not only present data but also provide narratives and insights, helping in faster and more informed decisions.
- Increased Accuracy: Chain-of-thought prompting can improve the accuracy of large language models on reasoning tasks. This could lead to improved performance in a variety of applications, such as customer service, medical diagnosis, and financial trading.
- New Products and Services: Chain-of-thought prompting could enable the development of new products and services that were not previously possible. For example, chain-of-thought prompting could be used to develop language models that can help doctors diagnose diseases or lawyers build legal cases.
Challenges & Considerations
As we adopt advanced prompting, it’s crucial to maintain a balance. We don’t want AI to overshadow user autonomy. Users should feel assisted, not directed.
Additionally, ethical considerations come to the fore. If AI starts driving conversations, there’s potential for bias or undue influence. Businesses need to ensure transparency in how these models function.
Future of AI-driven Product Development
Looking ahead, chain of thought prompting opens doors to innovative products:
- Education: Learning platforms can become more interactive, guiding students through concepts based on their pace and understanding.
- Healthcare: Virtual health assistants can provide more comprehensive advice, walking patients through symptoms, potential causes, and recommended next steps.
Incorporating advanced prompting in product development requires a clear business strategy. It’s not just about leveraging technology but aligning it with market needs and ensuring ethical considerations are in place.
Conclusion
The rapid advancements in AI present both challenges and opportunities. As chain of thought prompting becomes more prevalent, businesses stand to gain immensely. But with great power comes great responsibility. It’s upon us to harness this potential responsibly, ensuring we create value for users while upholding ethical standards.
Reference:
Ji, X., Yang, Y., & Liu, K. (2022). Unlocking the Power of Large Language Models: Chain-of-Thought Prompting for Reasoning Tasks. arXiv preprint arXiv:2203.07611.