Building an effective chatbot involves understanding various key aspects, including the training of Large Language Models(LLMs), incorporating memory, and production considerations.
Training
Language models (LLMs) undergo two main phases of training:
Pre-training: In this phase, the model learns to predict the next word in a sentence using large-scale datasets. This helps the model acquire general understanding of language.
Fine-tuning: This phase adapts the model to specific tasks. For chat models, the fine-tuning task is typically Conversational, where the model learns to engage in dialogues effectively.
Memory
LLMs do not inherently possess memory, meaning they cannot remember previous interactions or conversations. Developers can incorporate memory features to maintain context throughout the interaction.
For more insights, see the LangChain course by deeplearning.aion developing chatbots.
Production
When deploying a chatbot to production, it’s essential to ensure that it provides safe and non-toxic responses.
For more details on setting up effective moderation practices, refer to the ‘Moderation’ chapter in the Building Systems with ChatGPT course by deeplearning.ai.
To learn more You can find more details about the contents of each course here: Master Chatbot Development with DeepLearning.AI Courses.