This title slide is for a "转正述职报告" (Probationary Period Performance Report). It includes placeholders for name, department, and position, plus an entry date of July 1, 2025, and report date of December 16, 2025.

转正述职报告

姓名[请填] | 部门[请填] | 职位[请填] 入职日期：2025年7月1日 | 报告日期：2025年12月16日

The slide introduces the presenter's Master's in Computer Science and professional background as an AI Algorithm Engineer. It overviews the 6-month probation period, covering responsibilities in RAG development and data modules, plus a summary of efficient task completion with positive reviews.

自我介绍与试用期概述

• 学历：计算机科学硕士
• 专业背景：AI算法工程师
• 试用期时长：6个月
• 岗位职责：RAG开发、数据模块
• 表现总结：高效完成任务，获好评

This slide presents a 2025 work phase timeline from July to December, beginning with onboarding adaptation and team familiarization. It continues with knowledge base construction (Aug-Sep), RAG optimization (Oct), prediction/synthesis module development (Nov), and results summary for probation review (Dec).

工作阶段划分

2025年7月: 入职适应阶段 快速适应公司环境，熟悉岗位职责与团队协作方式。 2025年8-9月: 知识库搭建 构建知识库体系，为RAG等模块提供数据基础。 2025年10月: RAG优化 优化RAG检索框架，提升知识召回准确性和效率。 2025年11月: 预测与合成模块 开发参数预测和数据合成模块，实现核心功能。 2025年12月: 成果汇总 总结试用期工作成果，准备转正述职报告。

Source: 试用期工作流程

This slide highlights enterprise knowledge base building with efficient storage, RAG integration for precise retrieval, multi-source data fusion, 25% accuracy gains, and sub-1-second response times. It also notes successful validation in 3 real business cases.

知识库与RAG成果

{ "features": [ { "icon": "📚", "heading": "构建企业知识库", "description": "搭建企业级知识库，支持高效信息存储和管理。" }, { "icon": "🔍", "heading": "支持RAG检索", "description": "集成RAG技术，实现精准知识检索与生成。" }, { "icon": "📈", "heading": "准确率提升25%", "description": "优化算法，知识检索准确率提升25%。" }, { "icon": "🌐", "heading": "集成多源数据", "description": "融合多源异构数据，确保知识全面完整。" }, { "icon": "⚡", "heading": "响应时间<1s", "description": "系统优化，检索响应时间控制在1秒以内。" }, { "icon": "💼", "heading": "3个实际案例", "description": "已在3个业务场景成功应用并验证效果。" } ] }

The Parameter Prediction Module slide showcases 92% model accuracy and real-time prediction for 10+ parameters. It also highlights million-level data processing and an average prediction error below 5%.

参数预测模块

• 92%: 模型准确率

开发预测模型准确率

• 10+: 参数实时预测

支持10+参数实时预测

• 百万级: 数据处理量

处理达百万级数据

• <5%: 平均预测误差

性能图表误差控制 Source: 转正述职报告

The slide presents a data synthesis framework workflow with four stages: data cleaning (removing noise and standardizing), feature extraction (key vectors), synthesis generation (via GAN-like models), and quality verification (assessing authenticity, diversity, and consistency). Outputs progress from clean datasets to a verified, diversified synthetic dataset achieving 80% overall automation.

数据合成框架

{ "headers": [ "阶段", "主要任务", "输出/指标" ], "rows": [ [ "数据清洗", "去除噪声、缺失值处理及标准化", "清洁数据集" ], [ "特征提取", "自动提取关键特征向量", "高质量特征集" ], [ "合成生成", "基于生成模型（如GAN）合成数据", "初始合成数据集" ], [ "质量验证", "评估真实性、多样性及一致性", "验证通过的多様化数据集（整体自动化率80%）" ] ] }

Source: 转正述职报告 PPT

The "Chart Summary" slide showcases three key stats: 5 modules completed with all core modules delivered. It also highlights a 30% efficiency improvement and 110% KPI achievement rate, exceeding annual targets.

图表汇总

• 5个: 模块完成

核心模块全部交付

• 30%: 效率提升

工作效率显著提高

• 110%: KPI达成率

超额完成年度目标 Source: 成果统计

The slide's left column outlines capability improvements, including significant gains in Python/ML skills, deployment experience, business requirement understanding, and daily 2-hour self-study. The right column identifies a deficiency in deep learning knowledge and recommends specialized training plus project practice for improvement.

能力提升与不足分析

| • 技术：Python/ML技能显著提升

• 工程：部署与优化经验积累
• 业务：需求理解能力增强
• 自学：每日坚持2小时学习 | • 不足：深度学习知识需加强
• 改进：专项训练 + 项目实践应用 |

Source: 转正述职报告

The slide "试用期心得与反思" summarizes probationary period insights in four key areas. It emphasizes proactive self-driven learning of new technologies, business alignment with company needs, active team communication and contributions, and a growth mindset through iterating on failures.

试用期心得与反思

• 学习主动性：自驱探索新技术
• 业务导向：紧扣公司需求
• 团队协作：积极沟通贡献
• 成长思维：从失败中迭代

The slide outlines post-probation goals, including leading AI projects and producing 2 papers by 2026. It details personal plans to specialize in RAG and large model technologies, plus commitments to efficient delivery and ongoing frontier innovation.

转正后工作目标与规划

• 2026目标：主导AI项目，产出2篇论文
• 个人规划：深耕RAG+大模型技术
• 工作承诺：高效交付成果
• 创新承诺：持续推动前沿探索

The slide titled "谢谢聆听！" thanks the audience for listening. It invites questions, lists contact placeholders for email and phone, and expresses hope for permanent employment to continue contributing.

谢谢聆听！

谢谢聆听！

欢迎提问 联系：邮箱[请填] | 电话[请填] 期待转正，继续贡献！🙏

Source: 转正述职报告