This paper explores the potential of generative AI to streamline clinical documentation by generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes. Clinical documentation is crucial for effective healthcare delivery but is time-consuming and burdensome for healthcare professionals, leading to burnout, medical errors, and compromised patient safety. Generative AI, leveraging natural language processing (NLP) and automatic speech recognition (ASR), can transcribe patient-clinician interactions and generate draft clinical notes, capturing key patient information. The study presents a case study using NLP and ASR technologies to transcribe patient-clinician interactions and generate structured clinical notes using large language models (LLMs). Advanced prompting techniques were used to guide LLMs in generating comprehensive and structured clinical notes. The study highlights benefits such as time savings, improved documentation quality, and enhanced patient-centered care. Ethical considerations, including patient confidentiality and model biases, were addressed. The methodology involved data collection from patient-clinician interactions, transcription using ASR models, and utterance classification to separate patient and clinician speech. Prompt engineering techniques, including zero-shot and one-shot learning, were used to guide LLMs in generating structured clinical notes. Models such as GPT-4, Claude V3, Llama, and Mixtral were evaluated for their performance in generating SOAP and BIRP notes. The study found that GPT-4 consistently achieved superior performance in generating accurate and comprehensive clinical notes, while other models showed varying levels of performance. The results suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care. The paper also discusses challenges in implementing generative AI in clinical documentation, including data quality, privacy and security concerns, model interpretability, reliability, regulatory compliance, and the need for human oversight. Addressing these challenges requires collaboration among technology developers, healthcare professionals, regulatory bodies, and policymakers to ensure responsible and effective deployment of generative AI in healthcare settings. The integration of generative AI in clinical documentation has the potential to improve patient care, enhance clinical decision-making, and transform healthcare delivery.This paper explores the potential of generative AI to streamline clinical documentation by generating SOAP (Subjective, Objective, Assessment, Plan) and BIRP (Behavior, Intervention, Response, Plan) notes. Clinical documentation is crucial for effective healthcare delivery but is time-consuming and burdensome for healthcare professionals, leading to burnout, medical errors, and compromised patient safety. Generative AI, leveraging natural language processing (NLP) and automatic speech recognition (ASR), can transcribe patient-clinician interactions and generate draft clinical notes, capturing key patient information. The study presents a case study using NLP and ASR technologies to transcribe patient-clinician interactions and generate structured clinical notes using large language models (LLMs). Advanced prompting techniques were used to guide LLMs in generating comprehensive and structured clinical notes. The study highlights benefits such as time savings, improved documentation quality, and enhanced patient-centered care. Ethical considerations, including patient confidentiality and model biases, were addressed. The methodology involved data collection from patient-clinician interactions, transcription using ASR models, and utterance classification to separate patient and clinician speech. Prompt engineering techniques, including zero-shot and one-shot learning, were used to guide LLMs in generating structured clinical notes. Models such as GPT-4, Claude V3, Llama, and Mixtral were evaluated for their performance in generating SOAP and BIRP notes. The study found that GPT-4 consistently achieved superior performance in generating accurate and comprehensive clinical notes, while other models showed varying levels of performance. The results suggest that generative AI has the potential to revolutionize clinical documentation practices, alleviating administrative burdens and enabling healthcare professionals to focus more on direct patient care. The paper also discusses challenges in implementing generative AI in clinical documentation, including data quality, privacy and security concerns, model interpretability, reliability, regulatory compliance, and the need for human oversight. Addressing these challenges requires collaboration among technology developers, healthcare professionals, regulatory bodies, and policymakers to ensure responsible and effective deployment of generative AI in healthcare settings. The integration of generative AI in clinical documentation has the potential to improve patient care, enhance clinical decision-making, and transform healthcare delivery.