You have converted 10,000 documents into embeddings and stored them in a vector database. A user asks a question. Explain exactly how the system finds the most relevant chunks to send to the LLM.
Could you answer this clearly in an interview?
Not just say:
"We perform semantic search."
But explain:
- what happens to the user's question
- why the question must be converted into an embedding
- how it is compared with stored document embeddings
- what cosine similarity is doing
- how the chunks are ranked
- what Top-K retrieval means
- and what is finally sent to the LLM
You could easily answer if you read this article:
Build a RAG System in Google Colab Before Your Next AI Interview
The article makes you build the entire retrieval flow yourself:
Documents
↓
Chunks
↓
Embeddings
↓
User Question
↓
Query Embedding
↓
Cosine Similarity
↓
Ranked Chunks
↓
Top-K Results
↓
LLM Prompt
And this is exactly why building a small RAG system before an AI interview is useful.
Many candidates know the architecture diagram.
Far fewer can explain what actually happens between:
User Question → Vector Search → Retrieved Context
Once you implement it yourself, questions about embeddings, similarity search, Top-K retrieval, chunking, and context construction become much easier to answer.
But here is the follow-up question interviewers may ask:
If the most relevant chunk is ranked #6, but your system retrieves only Top-5 chunks, what happens? How would you improve the system?
Now the discussion moves into:
- choosing
top_k - retrieval recall
- reranking
- hybrid search
- better chunking
- metadata filtering
- retrieval evaluation
That is where understanding RAG becomes more important than simply knowing how to call a vector database.
If you're preparing for AI/ML interviews, you can continue learning about embeddings, vector search, and RAG here:
Embeddings and RAG Interview Preparation
And if you'd like to practice these concepts through live discussions and interview questions:
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