Context Stuffing
Including extensive background information in AI prompts to improve response quality.
Definition
Context stuffing is the practice of including extensive relevant information—documents, examples, guidelines, or background data—in AI prompts to improve response quality. Rather than relying solely on the model's training, context stuffing provides specific knowledge needed for the task at hand.
This technique leverages models' ability to use in-context information, enabling customization without fine-tuning. The approach has become standard practice as context windows have grown from thousands to millions of tokens.
Why It Matters
Context stuffing allows immediate AI customization without the cost and complexity of fine-tuning. It's the fastest path to AI that understands your specific domain, products, or processes.
For teams deploying AI, effective context stuffing often determines whether AI outputs are generic or genuinely useful.
Examples in Practice
A support AI receives complete product documentation as context, enabling accurate answers about specific features without custom training.
A writing assistant is stuffed with brand guidelines, ensuring outputs match company voice and terminology.
A code assistant receives the full codebase as context, enabling suggestions that follow existing patterns and conventions.