Chatbot Powered by ChatGPT

RAG Implementation and Prompt Engineering #

To implement RAG, we employed a two-step process: retrieval and generation. In the retrieval step, semantic similarity is the metric used to identify relevant blog content. This enabled the chatbot to retrieve accurate and up-to-date information to support its responses. We carefully engineered the prompts to encourage the chatbot to reference the blog content and reduce the risk of hallucination.

The generation step involved ChatGPT, which generated responses based on the retrieved information. By combining retrieval and generation, the chatbot was able to provide contextually relevant and accurate answers to user queries. This approach allowed us to strike a balance between the creativity of language models and the reliability of retrieved information.

Challenges and Solutions #

One of the main challenges was mitigating the issue of hallucination, where the chatbot generates responses that may not be accurate or reliable. To address this, we focused on prompt engineering and fine-tuning the retrieval mechanism to prioritize factual information from the blog content.

Another challenge was ensuring that the chatbot could handle a wide range of user queries. We implemented a robust intent classification system to understand user intents and direct them to the appropriate sections of the blog content. Additionally, we continuously monitored user interactions and collected feedback to improve the chatbot’s performance over time.