Prompt engineering

The world of artificial intelligence (AI) is a rapidly changing one, with new tools emerging at breakneck speed. Generative models have quickly emerged as groundbreaking tools with the capability to produce fresh content such as stories, conversations, images and even music. Publicly available generative AI applications are now creating output that is virtually nearly indistinguishable from human efforts. 

It’s only going to get bigger and better. A 2022 McKinsey survey shows that AI adoption has more than doubled over the past five years, and investment in AI is increasing dramatically. At the forefront of gen AI innovation is prompt engineering – an essential discipline that guides generative AI systems to produce desired outcomes. 

Earlier this year, Air Canada was held liable for its chatbot giving bad advice, even though the airline claimed the LLM was responsible for its own actions. And there’s the time a prankster tricked a Chevrolet chatbot into selling him a car for one dollar. Poor customer service experiences can lead to a loss in revenue from just one bad interaction. Research from the Qualtrics XM Institute found that poor customer service could collectively result in losses of $3.7 trillion annually. And internal use of chatbots based on LLMs is increasing, sometimes leading to failure, as illustrated in another case of the Samsung workers who unwittingly leaked confidential data to ChatGPT while asking it to help them with tasks. 

This demonstrates the real need for meticulous prompt engineering to avoid such failures. Crafting prompts that effectively guide AI models require creativity, experimentation and a solid understanding of user needs. Moreover, with the rapid advancements in generative AI, staying on top of recent trends and techniques is paramount to really understanding and maximizing the full potential of these models. 

EVOLVING TRENDS

As the demand for AI-driven solutions continues to grow, so do the challenges associated with prompt engineering. Adapting to evolving trends and techniques in prompt engineering and conversational models is essential to remain competitive in the AI landscape. 

SPS has embraced prompt engineering as a key part of its AI strategy, using it to deliver innovative solutions that redefine customer experiences. Through extensive experimentation and learning, SPS has discovered the importance of refining prompts to align with specific use cases and user preferences. 

The translations lacked appropriateness for a telecom support agent, sounding too formal and robotic – it misses the sentiment. GPT occasionally provided unnecessary explanations along with the translations. On a second attempt, the prompt was refined to provide GPT with more context, instructing it to adopt the persona of an experienced German customer support agent (see table). While the quality of the translations improved in relevance and sentiment, GPT still generated non-required information and occasionally attempted to reply to the Vietnamese instructions instead of communicating them back in German. 

On further attempts, additional adjustments were made to instruct GPT not to respond to the Vietnamese instructions but only to paraphrase them back in German. With clearer instructions, GPT's translations significantly improved in relevance, tone, and consistency, demonstrating success in overcoming language barriers. Through iterative prompt engineering and providing clear context, SPS managed to improve the accuracy and naturalness of the translations significantly. 

These examples demonstrate the need for clear and iterative prompts as well as contextual information. 

LESSONS LEARNED - DATA EXTRACTION

In another project involving data extraction from legal documents in French, there were real challenges due to unclear project rules and complex output requirements. The team broke down the task into shorter instructions, providing detailed information at every prompt. The first example shows how successful good prompt engineering and clear paraphrasing of the context is for the beneficial outcome of the LLM. The second example emphasizes the importance of precise instructions and understanding of project rules to achieve accurate data extraction from legal documents. Like a physical team, it is important to assign tasks accurately, reduce complexity by breaking tasks down and communicate expected outcomes & goals in detail.