RAG is only as good as the data you feed it. 📄➡️🤖 I am excited to share that I’ve completed the Build an AI-Powered Document Retrieval System with IBM Granite and Docling lab from IBM SkillsBuild! While my previous work focused on the RAG pipeline, this lab went deeper into the most critical step: Document Parsing. We often forget that real-world data isn't clean text—it's locked in complex PDFs and formatted documents. What I built in this hands-on lab: 🔹 Advanced Parsing with Docling: I used Docling to not just "read" text, but to understand the structure of documents, preserving the context for the AI. 🔹 Granite Power: Leveraged IBM Granite models (granite-embedding-30m-english) to create high-quality vector embeddings. 🔹 Seamless Integration: Orchestrated the entire workflow using LangChain to connect the parsed data with the retrieval engine. This skill allows me to build AI agents that don't just "guess" answers but can accurately retrieve information from complex business documents. Technical breakdown of what I built: 🔹 Orchestration: Used LangChain to manage the flow between the user, the database, and the model. 🔹 Embeddings: Leveraged IBM Granite models (granite-embedding-30m-english) to convert text into vector representations. 🔹 Data Processing: Implemented document loading and chunking strategies to optimize context windows. 🔹 Synthesis: Created a system that retrieves relevant data and generates accurate, fact-based summaries. This experience has given me the practical skills to build AI applications that are not just "smart," but also accurate and domain-specific.