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Context injection is a sophisticated approach to emphasize certain pieces of text. The idea is to surround them with relevant, supporting information. The core idea is that important concepts deserve more explanation, supporting details, and contextual reinforcement. Rather than simply repeating text or marking it as important, context injection aims to amplify the significance of key information through additional semantic content.
In practice, context injection works by adding layers of supporting sentences around the original text, with the number and depth of these layers determined by the assigned weight. For example, if “machine learning expertise” has a high weight, the system might inject context like “I have implemented various machine learning algorithms in production environments” and “My machine learning work has directly contributed to business outcomes.” These injected contexts serve multiple purposes — they reinforce the original concept, provide evidence of expertise, and create a richer semantic environment around the key term.
The elegance of context injection lies in how it mirrors natural human communication. When people want to emphasize something important, they don’t repeat themselves — instead, they provide supporting details, examples, and explanations.
