A company is building a customer support chatbot using a pre-trained language model. The goal is for the bot to answer highly technical questions by referencing a large, private database of product manuals. The team implements a system where, for each user query, relevant passages from the manuals are retrieved and provided to the model as context. However, they observe that the chatbot frequently ignores the provided passages and instead generates generic, often incorrect, answers based on its original pre-training data. The team's training approach involved fine-tuning the entire language model on the raw text of all the product manuals, hoping it would 'absorb' the knowledge. Based on this information, critique the team's training strategy. What is the most likely reason their approach is failing, and what principle have they misunderstood about integrating a model with an external knowledge source?