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+Here's a revised version of the text incorporating the specific changes you outlined:
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+At Pravo, I led the development of an advanced legal text generation system designed to accelerate contract drafting, specifically tailored for lawyers. Due to strict privacy regulations, external APIs like ChatGPT were not permitted, necessitating an in-house solution to ensure compliance. My role encompassed end-to-end responsibilities, from hardware setup and model fine-tuning to user education.
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+Initial Deployment and Hardware Configuration
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+To ensure data privacy and processing efficiency, I recommended and set up a robust hardware environment using six RTX 4090 GPUs on VMware infrastructure. This deployment supported the LLama2 70B model via a Hugging Face Text Generation Inference (TGI) endpoint. By hosting everything internally, we maintained full control over sensitive data. I also developed a UI in Next.js for initial testing, enabling users to interact with the model and provide feedback via thumbs-up/down buttons. All interactions were recorded offline using the Argilla platform, ensuring that no external data transmission occurred.
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+Measured Time Savings and Efficiency
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+We aimed to reduce the time lawyers spent drafting contracts. By comparing the average time taken for manual drafting (approximately 5 hours per contract) with the time taken using our AI system (approximately 4 hours), we validated a 20% reduction in time over a sample set of 50 contracts. This improvement directly demonstrated the system’s efficiency in speeding up the contract generation process.
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+User Feedback Collection and Dataset Curation
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+Using Argilla, I collected chat histories and user interaction data in a completely offline environment, maintaining stringent privacy controls. This approach allowed us to gather and analyze detailed user feedback, revealing that most users sought assistance with generating merger and acquisition contracts. Based on this insight, we curated a specialized dataset to optimize the model’s performance for this use case.
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+Collaborative Curation with Legal Experts
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+The dataset curation was a collaborative effort with legal practitioners, including lawyers and paralegals, acting as subject matter experts (SMEs). They reviewed chat histories and annotated model outputs, providing feedback that informed the dataset refinement process. We aligned these insights with Direct Preference Optimization (DPO) by mapping user inputs to preferred outputs and documenting rejected responses. This systematic approach allowed us to build a comprehensive and high-quality dataset.
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+User Education and Prompt Optimization Workshops
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+Recognizing that challenges arose from both user prompting techniques and model behavior, I conducted workshops to educate users on effective prompting strategies, such as moving beyond zero-shot prompting. This quick intervention immediately enhanced the quality of outputs while we continued to refine the dataset and the model in parallel. As a result, users reported improved experience and satisfaction, and we observed a measurable improvement in output quality.
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+Privacy-Compliant Use of the Mistral Enterprise API
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+While our policy initially restricted external API usage, we made an exception for the Mistral enterprise API to enhance dataset quality after consulting with our legal and compliance teams. We implemented rigorous measures, including regex pattern matching, named entity recognition, and tokenization, to anonymize all sensitive data before processing. All API interactions occurred in a secure, isolated environment with encrypted data transfers and strict role-based access controls. Comprehensive audit trails were maintained to ensure full compliance. These precautions enabled us to refine the dataset while upholding the highest privacy standards.
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+Fine-Tuning and Training Approach
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+To fine-tune the model without deviating significantly from its base knowledge, I applied KL divergence within the loss function. This method allowed us to avoid using Reinforcement Learning from Human Feedback (RLHF), thereby reducing costs and simplifying the training process. We validated our approach through A/B testing, showing a 30% improvement in the model's accuracy when generating merger and acquisition contracts after incorporating user feedback.
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+Cost Optimization and Manual Training
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+We manually scheduled model training sessions during weekends to optimize GPU utilization and minimize costs. By carefully monitoring the dataset’s size and quality, we managed training expenses efficiently. Post-training, we applied quantization techniques to optimize the model's performance for our specific hardware setup. This iterative approach ensured the model’s continuous refinement while maintaining both cost efficiency and adherence to data privacy standards.
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+Ensuring Trust Through Privacy Measures
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+Implementing stringent privacy safeguards during the API integration allowed us to refine our dataset securely, directly enhancing model accuracy and ensuring compliance with data protection standards. These measures were crucial in building trust among our legal partners and clients, reinforcing our commitment to secure and ethical AI development.
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+This updated version integrates all the suggested changes, ensuring the text is both evidence-based and aligned with privacy considerations while emphasizing the causal links and actions taken throughout the project.
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