AI Agent & RAG Integration
Dominik Huber
Overview
Portfolio demo of a retrieval-augmented AI workflow that turns a question into a grounded answer using document retrieval, reranking and source-aware generation.
The Problem
Generic LLM responses can be ungrounded when answers depend on a private or domain-specific knowledge base.
The Solution
A RAG pipeline retrieves relevant chunks, reranks context and generates source-aware responses through a modular agent workflow.
Engineering Highlights
Semantic retrieval pipeline
Context building and reranking
Grounded answer generation
Traceable modular architecture
Tech Stack
RAG, LLM, Vector Search, Python, API Integration
Portfolio demo / concept work. No client data or confidential information.
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Posted Sep 21, 2026
Portfolio demo of a retrieval-augmented AI workflow that turns a question into a grounded answer using document retrieval, reranking and source-aware generation.
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