Large language models (LLMs) do not understand context in the same way humans do. Humans interpret context through lived experiences, emotions, and an awareness of intent, whereas LLMs process context by identifying statistical patterns in text. For example, when a human reads a conversation, they infer unspoken meanings, cultural nuances, and emotional tones. An LLM, however, analyzes sequences of tokens (words or characters) to predict likely continuations based on its training data. While this allows LLMs to generate coherent and contextually relevant responses, it lacks the depth of human comprehension, which is rooted in consciousness and real-world understanding.
LLMs handle context by using mechanisms like attention layers and token windows. Attention layers allow models to weigh the importance of different words in a text sequence, enabling them to focus on relevant parts of a prompt. For instance, in a dialogue like "User: It’s freezing here. Assistant: Want me to adjust the thermostat?", the model links “freezing” to temperature control because those associations appear frequently in training data. However, LLMs have fixed token limits (e.g., 4,000–32,000 tokens), so they cannot retain information beyond that scope. If a conversation exceeds the limit, earlier context is discarded unless explicitly re-referenced. This contrasts with humans, who can recall broader context even in lengthy discussions, integrating prior knowledge and situational awareness.
For developers, this means designing systems that compensate for LLM limitations. For example, when building a chatbot, you might track key context points (like user preferences) externally and inject them into prompts as needed. If a user mentions allergies early in a conversation, storing that detail in a database and appending it to later queries ensures the LLM stays aware of it. Additionally, breaking complex tasks into smaller steps helps avoid overwhelming the model’s token window. Testing edge cases—like ambiguous references (“I need that file from yesterday”)—is critical, as LLMs might struggle without explicit cues. While LLMs are powerful tools, their “context awareness” is a simulation built on pattern matching, not true understanding.