Large language models (LLMs) are increasingly used in long-running user-facing applications where responses may need to maintain a consistent interaction style. Prompting is easy to reuse but often weak, while fine-tuning and representation interventions can be stronger but are typically model-specific. We present PeLA, Prompt-Enhanced Logit Adaptation, a parameter-efficient framework for Big Five personality-trait steering. PeLA keeps the base LLM frozen, uses a personality prompt to specify the desired trait direction, and applies a learned logit adapter during decoding. The adapter contributes a base-relative residual controlled by a token-position gate, refining the prompt-conditioned distribution only when intervention is useful. Because the control module operates in output-token space, it can be reused across models with identical tokenizer vocabularies and token-id order. On open-ended PersonalityBench questions, PeLA improves macro-average target-direction scores over a strong Personality Prompt baseline on Qwen3-8B and Qwen2.5-7B, while cross-size experiments demonstrate tokenizer-compatible transfer within both the Qwen3 and Llama-3.2 families after validation-based scale calibration. PeLA narrows the gap to model-specific steering while preserving a frozen base model.