Today, I continued building Nexa AI, an AI-powered career assistant that helps students and beginners explore career paths and create learning roadmaps. Today’s progress: ✅ Improved the Career Recommendation Agent ✅ Added dynamic career matching across different fields ✅ Created a scoring system to recommend suitable careers based on user profiles ✅ Added career explanations, skills to learn, roadmaps, and project suggestions ✅ Fixed and tested the agent workflow The project is getting closer to the final demo, and I’m excited to keep improving it! 🚀 #AgenticAI #AIProjects

Today I worked on improving Nexa AI, my AI career assistant. Progress: ✅ Made the career recommendation system more dynamic ✅ Added support for different career backgrounds ✅ Improved how user skills and goals are matched with career paths ✅ Continued building the agent workflow The goal is to make Nexa AI useful for more than just technical users and help beginners explore different career opportunities. #AgenticAI #AIProjects

Over the past two days, I’ve been diving deep into AI product development and Retrieval-Augmented Generation (RAG)—gaining both strategic frameworks and hands-on experience building real-world AI solutions. 🚀 Workshop 1: AI Product Thinking & RAG Foundations The opening session focused on the bridge between business requirements and technical execution—understanding how successful AI products are actually designed and built. Key Learnings: Idea Translation: Converting raw concepts into clearly defined AI project scopes. Problem-First Mindset: Identifying core business challenges before picking the tech stack. Data Strategy: Defining data requirements and effective ingestion strategies. LLM Evaluation: Comparing model capabilities, latency, and cost structures across different providers to select the best fit. Parameter Tuning: Mastering OpenAI configuration parameters like Temperature, Top-P, and Max Tokens. RAG Architecture: Understanding the end-to-end workflow 🤖 Workshop 2: Building a RAG Chatbot with LlamaIndex The second workshop was all about hands-on implementation in Google Colab, building a functional RAG pipeline from scratch. Tech Stack: ⚙️ LlamaIndex: Document ingestion, chunking, indexing, retrieval orchestration, and context management. ⚙️ Gradio: Rapid deployment of a clean, interactive chatbot user interface. Key Takeaway: LlamaIndex simplifies complex RAG engineering tasks that would otherwise require extensive custom boilerplate. It allows developers to focus on solving core business problems rather than managing underlying infrastructure. 💡 Practical Project: DDS HR Chatbot To apply these concepts directly, I built an HR Chatbot for DDS using the RAG architecture. Key Features & Impact: Retrieves answers directly from internal HR documents. Delivers accurate, context-aware responses. Minimizes hallucinations by grounding answers strictly in company knowledge. Demonstrates how domain-specific AI can transform internal knowledge access and employee support.

Today I continued improving Nexa AI, my AI career assistant project. Instead of only adding features, I focused on making sure the agent workflow is clear and useful. Today I worked on: ✅ Reviewing the role of each agent ✅ Planning how Nexa can support different career backgrounds ✅ Preparing demo scenarios ✅ Organizing the next steps for the project Building AI systems is not just about the model — it’s about creating a workflow that can understand users and provide meaningful results. Looking forward to continuing the development! 🚀 #AgenticAI #AIProjects

Today I worked on improving Nexa AI’s backend and preparing the system for the next stage. What I did: ✅ Improved the career recommendation engine ✅ Cleaned the agent workflow ✅ Prepared Gemini AI integration ✅ Tested the system and fixed issues Building a strong AI application starts with creating a reliable foundation before adding more advanced features. 🚀 #AgenticAI #AIProjects