Use this path when you want to move from simple cloneable LLM apps to stronger agentic systems without getting lost in random demos.
Goal: learn how Hugging Face Spaces, Gradio, Streamlit, dependencies, secrets, and simple LLM app structure work.
Start with:
What to study:
- Where the app entry point lives.
- How dependencies are declared.
- How API keys and environment variables are handled.
- How the UI calls model or retrieval logic.
Goal: understand document ingestion, chunking, embeddings, vector stores, retrieval, prompting, and answer generation.
Start with:
What to study:
- PDF loading and text extraction.
- Chunking strategy.
- FAISS, ChromaDB, Qdrant, Pinecone, or other vector store usage.
- Prompt structure and citation handling.
- Separation between UI, retrieval, and generation code.
Goal: learn how retrieval becomes part of a tool-using workflow rather than a single question-answer call.
Start with:
What to study:
- Tool definitions.
- Agent state.
- Routing decisions.
- Retry/error paths.
- How retrieval is triggered and validated.
Goal: understand when multiple agents help, when they add complexity, and how orchestration is represented in code.
Start with:
What to study:
- Agent roles.
- Shared state or memory.
- Task delegation.
- Human-readable traces.
- Where the workflow can fail.
Goal: understand how model-facing tools can be exposed as reusable protocol servers and app wrappers.
Start with:
What to study:
- Tool schema design.
- Server/app boundaries.
- How the MCP server is launched.
- How an LLM client discovers and calls tools.
After studying three to five projects, build a small project with:
- One clean README.
- One reproducible setup path.
- One sample input.
- One obvious app entry point.
- One test or smoke-check script.
The best portfolio projects are not the biggest. They are the easiest to understand, clone, run, and extend.