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Part of the accelerator
RAG systems with ChromaDB and cross-encoder reranking is one workshop inside AI Engineer Career Accelerator Program.
See the full path with mentor reviews and a portfolio outcome.
A grounded chatbot needs more than embeddings. Scrape real websites, index them in ChromaDB, and rerank retrieved chunks with a cross-encoder so the LLM answers from facts, not fiction.
Engineers are learning here from
Build a production-ready RAG chatbot that scrapes websites, indexes content in ChromaDB, and reranks results with a cross-encoder for higher answer quality. Learn retrieval, reranking, metadata filtering, and trustworthy grounded responses.
Build a grounded RAG chatbot with ChromaDB, web scraping, and cross-encoder reranking.
What you'll ship
What you'll learn
Curriculum
Content ingestion
Set up the project, scrape real web pages, and see why a naive chatbot falls apart without grounding
The RAG pipeline
Build the full pipeline: embeddings, ChromaDB storage, retrieval, and grounded answer generation
Quality and scale
Tune retrieval quality and extend the pipeline to multiple source sites
Who it's for
who need a real RAG pipeline, not a LangChain quickstart that breaks in production
tired of hallucinating chatbots and want grounded answers with source attribution
curious about embeddings, vector stores, and reranking but unsure how they fit together
FAQ
No. The embedding and cross-encoder models used here (all-MiniLM-L6-v2, ms-marco-MiniLM-L-6-v2) run fine on CPU. They are small enough for a laptop.
ChromaDB runs locally with zero setup and persists to disk. It is perfect for learning and shipping small production workloads. The patterns you learn transfer directly to any vector database.
Yes, if you care about answer quality. Bi-encoder similarity search is fast but coarse. A cross-encoder compares the query against each candidate directly and is noticeably more accurate. You retrieve many, then rerank to a few.
No. RAG Fundamentals teaches the concepts. This course builds a real, scalable implementation with reranking and metadata filtering that production systems actually use.
Grounded retrieval is the foundation every serious AI system is built on.