The Software Engineer's Guide to Prompt Engineering and ChatGPT [video]

Nov 7, 2023 ·

Prompt engineering with ChatGPT is a technique to generate quality answers for open-domain conversation problems. It uses a transformer-based language model to generate natural language responses, taking into account the context provided in the question and using it to generate a believable reply. Prompt engineering involves fine-tuning a large language model to be able to better answer particular kinds of questions or conversations. The approach was first proposed by OpenAI at the OpenAI Summit 2020.

The idea behind prompt engineering involves curating and tailoring specific input contexts for the language model. A curated example might involve providing several sentences of context from a conversation or even a single sentence that encapsulates the gist of the conversation. By providing different input contexts, the language model can be conditioned to respond differently depending on the situation or context.

Due to its scalability and ability to generate more realistic and plausible answers, ChatGPT has been used in many applications such as customer support, virtual assistants, chatbots, and conversational AI. In comparison to rule-based systems, which require extensive domain knowledge, ChatGPT requires relatively less human resources to maintain. This makes ChatGPT a cost-effective option for companies that want to deliver quality customer service without having to invest heavily in training and maintenance costs.

At the same time, ChatGPT also has some challenges. For instance, the generated responses may sound too robotic due to lack of emotion and tone recognition. Additionally, ChatGPT still requires manual tuning to tune the parameters of the model to generate suitable output. Lastly, the quality of generated responses can vary depending on the input data it is trained on.

Overall, prompt engineering with ChatGPT is a promising technique for open-domain conversation problems. By tailoring specific input contexts, the language model can be conditioned to respond more accurately and realistically. It is a cost-effective solution for companies that want to provide quality customer service, virtual assistance, or conversational AI solutions. However, there are still some challenges associated with ChatGPT, such as lack of emotion recognition and manual tuning of parameters. Nevertheless, with further research, prompt engineering with ChatGPT could be an effective tool for open-domain conversation problems.