What this video shows
The tutorial builds each RAG step in plain Python: load text, split it into chunks, create embeddings, compare a query with stored vectors, place the selected context in a prompt and ask a local model for an answer. The small codebase makes data flow and failure points visible.
Use the implementation as a learning baseline. A production system still needs document parsing, stable identifiers, access control, retrieval tests, citation checks, monitoring and rules for stale or conflicting sources.
Use Ollama's embedding model documentation to verify the current API and supported embedding workflow. Pair the build with Learnetto's AI citation verification workflow before presenting retrieved claims to users.
What you will learn
- Build one transparent baseline before adding a framework.
- Evaluate retrieval separately from answer generation.
- Keep source identifiers with every chunk so an answer can point back to evidence.
- Test missing, stale, conflicting and access-restricted documents.
How to apply this safely
- Choose a small corpus whose correct answers and sources you can review.
- Run the tutorial pipeline and inspect the chunks selected for ten representative questions.
- Record retrieval misses, unsupported answers, latency and model cost or local resource use.
- Add citations, access checks and a refusal path before connecting the pipeline to user data.
Important limitations
- The tutorial favors a short teaching implementation over production parsing, authorization, observability and scale.
- Similarity search alone may miss exact identifiers, dates and terms that lexical or hybrid retrieval can find.
- A local model can still ignore retrieved evidence or state an unsupported answer.
- The sponsored segment is separate from the technical steps, and learners should evaluate the promoted program independently.
Sources to check
- How to Build RAG From Scratch in 50 Lines of Python Tech With Tim tutorial published October 30, 2026.
- Ollama embeddings documentation Current Ollama documentation for generating and using embeddings.
- Verify AI citations Learnetto's claim-level source verification workflow.
Continue learning on Learnetto
Verify AI citations
Check that each answer is supported by the retrieved source.
AI evals guide
Measure retrieval and answer quality with stable cases.
AI engineering courses
Compare deeper courses after building the baseline.