Generalized　zero-shot　learning　(GZSL)　addresses　the　challenge　of　recognizing　both　seen　and　unseen　classes　with　only　training　data　from　the　seen　classes.　While　the　large-scale　model　CLIP　holds　promise　for　GZSL,　a　significant　obstacle　remains:　the　scarcity　of　high-quality　prompts.　To　overcome　this,　we　present　a　novel　prompt　learning　approach　for　GZSL　that　leverages　semantic　information　to　guide　the　construction　of　a　generic　prompt　template　applicable　to　both　seen　and　unseen　classes.　Specifically,　we　propose　a　semantic-guided　prompt　tuning　network,　which　effectively　learns　the　prompt　template　using　semantic　knowledge,　enabling　its　utilization　across　seen　and　unseen　classes.　We　extensively　evaluate　our　approach　on　three　GZSL　datasets,　where　our　network　consistently　achieves　competitive　performance　across　all　three　datasets.　By　addressing　the　challenge　of　prompt　quality,　our　method　demonstrates　the　potential　of　CLIP　in　GZSL　tasks　and　highlights　the　importance　of　semantic　guidance　in　learning　effective　prompt　templates.