Tutorials Microsoft Agent Framework with Ollama Tutorial
RAG Fundamentals
RAG Fundamentals: free step-by-step lesson with examples, common mistakes, and interview tips — part of Microsoft Agent Framework with Ollama Tutorial on Toolliyo Academy.
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Microsoft Agent Framework with Ollama Tutorial · Lesson 41 of 100
RAG Fundamentals
Foundations & Ollama ✓ → .NET & Agents ✓ → RAG & Ops → Projects
RAG & Ops · 3 — Ship safely · ~6 min · RAG & Vector Search
What is this?
RAG (Retrieval-Augmented Generation) means: search your docs first, then put the best chunks into the prompt so the LLM answers with your facts.
Why should you care?
Local agents hallucinate less when they read your PDF/wiki chunks instead of guessing.
See it live — copy this example
Run CLI steps in a terminal. Run C# samples in a .NET 8 console or Web API project with Ollama running on localhost:11434.
// Pseudocode flow
// 1) embed question
// 2) vector search top 4 chunks
// 3) prompt = system + chunks + question
// 4) call Ollama chatWhat happened?
- Embedding turns text into vectors.
- Search finds similar chunks.
- The LLM only sees those chunks plus the question.
Practice next
- Pick one PDF or markdown folder for your local AI app.
- Write the 4-step flow on paper.
- build a fake retriever that returns 2 hard-coded chunks.
- Limit chunks to 3.
- Add “If unknown, say you do not know”.
Remember
RAG = retrieve then generate. Chunks beat whole files. Local vectors keep data private.
Policy Q&A
Employees ask leave-policy questions.
Outcome: Answers grounded in the handbook chunks.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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