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Context Loading Optimization Framework
A strategy for providing AI systems with the right amount of contextual information at the right time to maximize performance while minimizing hallucination and confusion.
How It Works
Instead of front-loading all available context, context is retrieved and loaded only when the AI determines it's relevant to the specific task being performed, similar to how you'd brief a junior employee.
Components
Identify what context is truly essential vs. nice-to-have
Structure context into discrete, referenceable chunks
Create retrieval triggers based on task requirements
Monitor output quality as context volume changes
Iteratively optimize context scope and timing
When to Use
When working with complex business processes, large datasets, or multi-step workflows where too much context can degrade performance.
When Not to Use
For simple, one-off tasks or when all context is genuinely needed for the task.
Anti-Patterns to Avoid
Example
“A customer service AI skill that only loads specific product documentation when a customer asks about that product, rather than loading the entire product catalog for every conversation.”