The article chronicles the journey of developing a chatbot designed to overcome the persistent limitations in typical conversational Artificial Intelligence systems. Frustrated by bots that could answer only immediate questions but failed to remember prior conversations or user preferences, the author set out to engineer a chatbot with a genuine sense of context. The goal was to develop a conversational agent that could recall previous inputs, adapt responses based on chat history, and create a more genuinely human interaction rather than simply reacting in isolation to each new prompt.
The exploration began with a decision between building a model from scratch or leveraging existing open-source models. Training a language model de novo proved impractical due to the significant resource demands. Instead, the author opted to fine-tune an open-source foundation, but the true leap in capability came with the adoption of retrieval-augmented generation (RAG). This approach offloads the need for the language model to memorize all possible knowledge; instead, it retrieves relevant data at runtime before producing contextually appropriate responses. By utilizing this retrieval step, the chatbot could dynamically access FAQs, historical records, or other key documents, making its conversations not only more accurate but also more aligned with the user´s ongoing context.
The implementation of RAG marked a turning point. User queries did not simply hit the language model directly. Instead, each question prompted a real-time search across curated informational resources. The model would synthesize the fetched information alongside the conversational context, generating responses that were both precise and tailored to prior exchanges. This architecture enabled the bot to remember user preferences and respond fluidly to the flow of conversation, significantly narrowing the gap between artificial and human dialogue. The article highlights not only technical decisions but also the challenges and experimental surprises that surfaced along the way, painting a vivid picture of progress in context-aware conversational Artificial Intelligence.
