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By enabling software bots to carry out activities that previously needed human intelligence, artificial intelligence (AI) has completely changed how we live and work. Prompt engineering has become a crucial component of this paradigm shift since the focus of NLP research has changed from conventional rule-based approaches to models built with deep learning in recent years.

Prompt Engineering Advancement: LokiBots using Prompt Engineering to trigger an RPA bot

Prompt engineering can be a powerful tool for triggering robotic process automation (RPA) bots to complete specific actions. At LokiBots, we’ve taken use of prompt engineering to let chatbots launch RPA bots directly from the chat window. With the use of this strategy, we were able to effortlessly combine chatbot and RPA bots, creating automation solutions that are both effective and efficient.

Using prompt engineering to activate RPA bots has a number of benefits, one of which is that it enables extremely configurable and targeted responses. Chatbots may be developed to instruct RPA bots to carry out incredibly precise tasks by carefully developing cues that can elicit certain responses from users.

Consider a situation where a user has to download a file, extract data from a certain website, and then create a report using that data. Previously, this would involve a lot of human labour and be prone to mistakes. But, using prompt engineering, we may design a chatbot prompt that will instruct an RPA bot to carry out these activities automatically.

With LokiBots, our chatbot can direct an RPA bot to visit a website like yahoo.com, download the specified files, and create a report based on the content of the file via a chatbot conversation.

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Empowering Business Efficiency: LokiBots’ Codex Enables Non-Technical Users to Customize RPA Workflows

LokiBots has transformed how non-technical users may integrate custom Python code into their RPA operations. LokiBots’ ability to write custom Python script before and after each bot step is one of its most creative features. This implies that non-technical users may modify the behaviour of their chatbots and automation workflows without learning a new programming language. Users may develop the appropriate python code to carry out a job by using natural language to explain what they want their bot to perform.

Assume a user requests that their chatbot retrieve the current system date. Instead of needing to write python code, the user may just say “extract current system date” in natural language. LokiBots’ Codex, which is integrated with LLMs (Language Model Models), will create the python code required to retrieve the system date. The user may then verify the code’s output to confirm that it is correct and, if satisfied, include it into the bot flow.

Overall, LokiBots’ ability to produce custom Python code using natural language is a strong tool that simplifies the creation and management of chatbots and automation workflows for businesses of all kinds.