Large language models excel at legal text generation but often produce hallucinations due to their sole reliance on parametric knowledge. Retrieval-augmented models mitigate this by providing relevant external documents to the model but struggle when retrieval is based only on past context, which may not align with the model’s intended future content. We introduce LexKeyPlan, a novel framework that integrates anticipatory planning into generation. Instead of relying solely on context for retrieval, LexKeyPlan generates keyphrases outlining future content serving as forward-looking plan, guiding retrieval for more accurate text generation. This work incorporates planning into legal text generation, demonstrating how keyphrases—representing legal concepts—enhance factual accuracy. By structuring retrieval around legal concepts, LexKeyPlan better aligns with legal reasoning, making it particularly suited for legal applications. Using the ECHR corpus as case study, we show that LexKeyPlan improves factual accuracy and coherence by retrieving information aligned with the intended content.