If your AI portfolio only includes a basic PDF chatbot, consider building more advanced projects. RAG now extends beyond uploading documents and simple question answering. Modern systems integrate websites, multiple retrieval strategies, images, structured data, agents, and enterprise knowledge sources. I always recommend end-to-end RAG projects that showcase a comprehensive understanding of the entire retrieval pipeline, not just the LLM component.
This article outlines three end-to-end RAG projects you can add to your AI portfolio.
End-to-End RAG Projects
Below are three projects that make strong additions to an AI portfolio.
1. AI That Can Chat With Any Website
Rather than uploading PDFs manually, develop an application that converts a website URL into a searchable knowledge base.
Here’s the architecture:
For implementation, consider using Python, LangChain or LlamaIndex, sentence-transformers, FAISS or Chroma, and an open-source LLM via Ollama.
A key engineering challenge is designing the ingestion pipeline. Websites often include navigation elements, repeated content, JavaScript-generated sections, and irrelevant text. Your system must extract relevant content before indexing.
Refer to this example to get started.
2. ChatGPT for Your Personal Documents
While this is a traditional RAG application, you can enhance it by designing a comprehensive document intelligence system.
The architecture could look like:
Recommended technologies include Python, LlamaIndex or LangChain, FAISS, Chroma, or Qdrant, an open-source embedding model, and Ollama.
The main enhancement is hybrid search. Rather than relying solely on vector similarity, combine semantic retrieval with keyword search. This approach improves search quality for queries where exact terms are important.
Refer to this example to get started.
Preparing for AI/ML Interviews?
If you are building RAG projects to enhance your AI portfolio, be prepared to explain your architecture, retrieval strategy, and design decisions during interviews. I recommend ‘Cracking Your First AI/ML Interview’ as a helpful resource for interview preparation.
3. Multi-Modal RAG Pipeline
Choose this project if you want to move beyond text-only RAG systems.
A multimodal system retrieves information from text, images, charts, tables, and other media, rather than treating documents as plain text. Modern RAG systems use multimodal embeddings to retrieve images and other media along with text.
A possible architecture is:
Recommended tools include Python, a vision-language model, an embedding model, a vector database, and a locally running open-source LLM or VLM.
For example, uploading an annual report with paragraphs, financial tables, and charts should allow a multimodal RAG system to answer questions that require understanding both written and visual information.
Refer to this example to get started.
The Takeaway
In summary, here are three end-to-end RAG projects to consider for your AI portfolio:
The objective is not quantity, but to demonstrate your ability to build reliable, end-to-end AI systems.
Thank you for reading this article on end-to-end RAG projects for your AI portfolio. For more AI and machine learning tips, you are welcome to follow me on Instagram.
