Designing a RAG Layer: A Visual Guide
Large Language Models are powerful, but they have a fundamental problem: they can only answer from what they were trained on. Ask about your company’s internal docs or anything out of distribution - and they’ll either hallucinate or admit they don’t know.
Retrieval Augmented Generation (RAG) fixes this by giving LLMs access to external knowledge at query time. Instead of hoping the model “knows” something, we find the relevant information and hand it directly to the model.
Try it yourself. Enter any question below and watch the entire RAG pipeline execute step-by-step:
Interactive RAG Pipeline
Enter a query and watch how RAG retrieves context and generates an answer
Ask a Question
Enter a query to see how RAG retrieves relevant context and generates an answer
Document Database (5 chunks)
RAG Overview
Retrieval Augmented Generation (RAG) combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, RAG systems fetch relevant documents at query time and include them in the prompt context. This grounds the model's responses in actual data, dramatically reducing hallucinations.
Vector Search Fundamentals
Vector search works by converting text into dense numerical representations called embeddings. These 768+ dimensional vectors capture semantic meaning - similar concepts end up close together in vector space. This allows finding relevant documents even when they don't share exact keywords with the query.
RAG vs Fine-tuning
RAG is preferable when you need up-to-date information, have frequently changing data, or need to cite sources. Fine-tuning is better for teaching the model a specific style, format, or domain-specific reasoning patterns that don't change often. RAG is also much cheaper - no GPU training required.
Entity Extraction
Before searching, an LLM analyzes your query to extract key entities and intent. This enables metadata filtering - if you ask about “Python errors”, we can filter to only Python-related documents before searching.
Query Vectorization
Your text question gets converted into a 768-dimensional vector (array of numbers) using an embedding model. This vector represents the meaning of your query in a way computers can compare mathematically.
Vector Similarity Search
We compare your query vector against pre-computed document vectors using cosine similarity. Documents pointing in similar directions score higher - even if they use completely different words.
BM25 Keyword Search
Simultaneously, we run traditional keyword search. This catches what semantic search misses: exact terms, acronyms, product names, error codes. A query for “RFC 7231” needs exact matching.
Reciprocal Rank Fusion
Results from both searches get combined. RRF is elegant - it only uses rank positions, not scores, so we don’t need to normalize between completely different scoring systems.
Prompt Assembly
Retrieved chunks are assembled into a prompt with your question. The LLM sees exactly what context it should use, with clear instructions to answer based only on that context.
Response Generation
Finally, the LLM generates a response grounded in the retrieved documents. It can cite sources, and if the documents don’t contain the answer, a well-designed system will say so rather than hallucinate.
The Key Insight
RAG trades model memorization for retrieval. Instead of fine-tuning a model on your data (expensive, static, no citations), you keep a searchable index of your content and fetch relevant pieces at query time.
This means:
- Always current - update your index, not your model
- Citable - every answer can reference its sources
- Scalable - add millions of documents without retraining
- Auditable - inspect exactly what context produced an answer
What’s Next
In future posts I’ll explore:
- How to chunk documents effectively
- Evaluating RAG quality with automated metrics
- Reranking with cross-encoders
- When RAG isn’t enough and you need agents
The demo above uses simulated data, but this architecture powers production systems from customer support to enterprise search.