L29- RAG Explained | Retrieval Augmented Generation, Embeddings, Cosine Similarity & Semantic Search

Lecture 29 of the AI for Software Engineers series — Bipin Kumar starts the RAG series, one of the most important and most interviewed topics in GenAI. Every company that builds with LLMs uses RAG. This lecture covers the complete theory before the hands-on coding begins.

🧠 What's Covered:
Why RAG Exists:
LLMs are trained on publicly available internet data. They cannot answer questions from your private documents — a company's internal HR policies, a hospital's patient records, a bank's internal guidelines. RAG bridges this gap by giving the LLM access to your documents at query time without retraining.
The Open Book Exam Analogy:
RAG works exactly like an open book exam. You have a 200-page book. A question arrives. You do not memorise the whole book — instead you find the right page, read the relevant section, and write the answer in your own words.
In RAG: the book is your document. Finding the right page is Retrieval. Combining the question with the retrieved content is Augmentation. The LLM writing the answer from that combined input is Generation.
What is an Embedding:
An embedding is the conversion of any text into a list of numbers — called a vector. Words or sentences with similar meanings produce similar vectors. Dissimilar words produce very different vectors.
Example: King, Queen, and Minister all have similar vector values because they share a conceptual domain. Virat Kohli and MS Dhoni form a different cluster. Apple and Banana form another. Car and Bike form yet another. These clusters form naturally when the embedding model is trained — no manual labelling needed.
OpenAI offers two embedding models: small (1536 dimensions) and large (3072 dimensions). More dimensions generally means finer distinctions between meanings.
Cosine Similarity — How Similarity is Measured:
Cosine similarity measures the angle between two vectors to determine how similar they are.
Three key cases to remember:
Score = 1: the two vectors point in exactly the same direction — the texts are identical or very similar.
Score = 0: the vectors are perpendicular — the texts are unrelated.
Score = -1: the vectors point in opposite directions — the texts have opposite meanings.
How Retrieval Actually Works:
Take your 200-page document and extract the text from each page. Convert every page's text into a vector using an embedding model. When a query arrives, convert the query into a vector the same way. Calculate cosine similarity between the query vector and every page vector. Sort all 200 scores from highest to lowest. Take the top K results — typically 3 to 5 pages. These are the most relevant chunks.
Augmentation — Combining Query and Retrieved Content:
The top K retrieved chunks are concatenated with the original query and the system prompt. This combined input is sent to the LLM. The LLM generates an answer based on all three: the question, the context from your document, and the role instructions from the system prompt.
Keyword Search vs Semantic Search vs Hybrid:
Keyword search: finds pages containing the exact word. Fast but misses synonyms and related concepts.
Semantic search: finds pages with similar meaning, even if the exact word is absent. Uses cosine similarity on embeddings.
Hybrid: combines both approaches. Generally the most robust for production use.
The Full RAG Roadmap — Coming Next:
Chunking → Embedding → Vector DB → Retrieval → Re-ranking → Generation → Agentic RAG → Evaluation. At least 2 questions from this topic appear in every senior GenAI interview.

⏭️ Next Lecture (Lecture 30):
👉 RAG Coding — Chunking strategies, Vector DB setup, and first working retrieval

💬 Questions about embeddings or cosine similarity? Drop them in the comments — Bipin replies!
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