This title slide for the "转正述职报告" (Probation Confirmation Report) lists placeholders for the employee's name, department, position, entry date of July 1, 2025, and report date of December 16, 2025. It features a tech blue-green background with the company logo.

姓名：[您的姓名]

部门：[您的部门] 职位：[您的职位] 入职日期：2025年7月1日 报告日期：2025年12月16日

科技蓝绿背景，公司Logo

Source: 转正述职报告 PPT

The slide introduces the presenter's Master's in Computer Science and background as an AI algorithm engineer. It outlines a 6-month probation period focused on model development and optimization, with a commitment to efficiently completing core tasks.

自我介绍与试用期概述

• 学历：计算机科学硕士
• 背景：AI算法工程师
• 试用期：6个月
• 职责：模型开发与优化
• 总结：高效完成核心任务

The slide presents a project timeline titled "Work Phase Division" spanning July to December 2025. It includes three phases: startup and requirements analysis (Jul-Aug), core module development (Sep-Oct), and optimization with verification (Nov-Dec).

工作阶段划分

2025年7-8月: 项目启动与需求分析 完成项目启动，开展需求调研，明确目标与范围，为开发奠基。 2025年9-10月: 核心模块开发 专注核心模块设计与编码，实现关键功能，提升系统性能。 2025年11-12月: 优化与成果验证 进行系统优化、测试验证，确保成果稳定可靠，准备交付。

Source: 试用期工作进度

This slide showcases key achievements in enterprise knowledge base and RAG, including support for 100,000+ documents, 95% retrieval accuracy, and multi-modal text/image queries with 30% faster responses. It also highlights performance optimizations for faster, more precise retrieval and successful production deployment boosting business knowledge efficiency.

知识库与RAG成果

{ "features": [ { "icon": "📚", "heading": "企业知识库构建", "description": "覆盖10万+文档，实现知识高效存储与管理。" }, { "icon": "🎯", "heading": "RAG检索准确率", "description": "达到95%准确率，大幅提升信息召回与匹配质量。" }, { "icon": "🔍", "heading": "多模态查询支持", "description": "兼容文本图像查询，提升响应速度30%。" }, { "icon": "📈", "heading": "性能全面优化", "description": "知识检索更快更准，助力业务智能决策。" }, { "icon": "🚀", "heading": "成果成功落地", "description": "系统投入生产，显著提高企业知识利用效率。" } ] }

The Parameter Prediction Module workflow outlines four steps: input data preprocessing for efficient import, automated multi-dimensional feature extraction, real-time AI model prediction with 20% accuracy improvement, and output post-processing for smoothed, validated results. This process supports optimized feature engineering and seamless real-time prediction outputs.

参数预测模块

{ "headers": [ "步骤", "描述", "关键效果" ], "rows": [ [ "输入数据", "接收并预处理原始参数数据", "高效数据导入" ], [ "特征提取", "自动提取多维关键特征", "特征工程优化" ], [ "模型预测", "AI模型实时进行参数预测", "准确率提升20%" ], [ "输出优化", "后处理平滑并验证结果", "支持实时预测输出" ] ] }

Source: 转正述职报告 PPT

The Data Synthesis Framework workflow outlines four stages: data collection and cleaning for standardized inputs, generative synthesis using GANs/diffusion models to create 500k high-quality datasets, multi-dimensional quality validation, and production deployment. Key outputs include >95% validation pass rate, confirmed data usability, and 40% overall cost savings.

数据合成框架

{ "headers": [ "阶段", "关键活动", "输出/成果" ], "rows": [ [ "数据采集", "收集并清洗原始数据源，确保多样性和代表性", "标准化数据集（输入基础）" ], [ "合成生成", "运用生成模型（如GAN/扩散模型）合成数据", "50万条高质量合成数据集" ], [ "质量校验", "多维度校验一致性、分布、真实度", "校验通过率>95%，数据可用性确认" ], [ "应用部署", "集成框架至生产环境，支持下游任务", "部署上线，整体成本节省40%" ] ] }

Source: 转正述职报告 - 工作总结

The stats slide summarizes key performance metrics: 95% RAG accuracy with superior retrieval precision and under 5% prediction error with minimal deviation. It also highlights a 40% boost in synthesis efficiency for faster data generation.

图表汇总

• 95%: RAG Accuracy

Superior retrieval precision achieved

• <5%: Prediction Error

Minimal forecasting deviation

• ↑40%: Synthesis Efficiency

Data generation speed boosted Source: 转正述职报告

This work summary slide highlights the completion of three major module developments. It emphasizes delivering high-value results that advanced project progress, earning team recognition and exceeding KPIs.

工作总结

• 完成3大模块开发
• 交付高价值成果，推动项目进度
• 获得团队认可，超额完成KPI

The slide highlights capability improvements in deep learning skills, DevOps proficiency, business insights, and daily 2-hour self-study. It identifies weak communication as a key deficiency, with a plan to enroll in training for enhanced collaboration and reporting within three months.

能力提升与不足分析

| • 技术：深度学习技能精进，掌握先进算法。

• 工程：DevOps工具熟练应用，提升部署效率。
• 业务：增强需求洞察，优化产品解决方案。
• 自学：每日坚持学习2小时，持续积累知识。（28 words） | • 不足：沟通能力需加强，表达不够流畅。
• 改进：报名参加专业沟通培训，提升团队协作与汇报技能，目标3个月内显著进步。（32 words） |

Source: 转正述职报告 PPT

This slide, titled "Probation Period Insights and Reflections," outlines key takeaways from the trial period. It highlights self-driven tech exploration, alignment with company goals, active team contributions, and embracing challenges with a growth mindset.

试用期心得与反思

• 自驱探索新技术，展现学习主动性
• 紧扣公司目标，体现业务导向
• 积极贡献分享，加强团队协作
• 拥抱挑战进步，践行成长思维

Source: 转正述职报告 PPT

The slide outlines post-probation work goals, including leading a sub-project and producing one paper by 2026. It details personal plans to deepen AI skills and contribute to open source, with a commitment to fully commit and create shared value.

转正后工作目标与规划

• 2026目标：领导子项目，产出1篇论文
• 个人规划：深化AI技能，贡献开源
• 承诺：全力以赴，共创价值

The conclusion slide, titled "Thank you for listening," features the message "Looking forward to regularization—let's move forward together! 🚀." It invites questions during the Q&A session and lists contact details via email and phone.

谢谢聆听

期待转正，一起前行！🚀

Q&A环节欢迎提问 联系方式：邮箱 / phone

Source: 转正述职报告 PPT