InstaVM

LLMs perform poorly when given redundant information, assigned tasks outside their strengths, operating near context limits, handling obscure topics lacking training data, or when developers fail to actively monitor the generated code. Effective LLM usage requires conserving context by eliminating duplicate inputs, delegating to the model's strengths (especially code generation over language tasks), maintaining awareness of accuracy degradation in long sessions, and maintaining active oversight rather than passively accepting output.

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