Context Stuffing
Context stuffing is the technique of loading relevant information — documents, data, or examples — directly into an AI model's prompt to give it the knowledge needed to answer accurately. Instead of relying on the model's training data alone, you "stuff" the context window with specific content the model should reference. While effective for small amounts of data, it becomes costly and less reliable as volume grows due to the "lost in the middle" effect where models miss details buried in long contexts.
Example
You paste your company's entire 20-page product manual into a ChatGPT prompt and ask "What is our return policy?" The model reads the manual from the prompt and gives an accurate answer. However, if you stuffed 100 pages of documentation, the model might miss a key clause buried in the middle — which is why RAG selectively retrieves only the most relevant passages instead.
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