The Architecture of RAG (Retrieval-Augmented Generation) for Enterprise Data

ChatGPT is smart, but it's amnesiac. It doesn't know your business. It doesn't know your Q4 revenue, your leave policy, or your client list.
RAG (Retrieval-Augmented Generation) is the industry-standard architecture to solve this. It connects the "Brain" (LLM) to "Long Term Memory" (Your Database).

How RAG Works: The Librarian Metaphor

Imagine the LLM is a brilliant professor who has read every book in the world, except your company's internal handbook.

1. The Question: User asks "what is our policy on remote work?"
2. The Retrieval (The Librarian): The system runs to the library (Vector DB), looks up "remote work", and pulls the 3 most relevant pages from the handbook.
3. The Context Injection: The system staples those 3 pages to the user's question.
4. The Generation (The Professor): The system says to the LLM: "Using ONLY these 3 pages, answer the user's question."
5. The Answer: The LLM reads the pages and synthesizes an accurate answer, citing page 12.

Key Concept: Vector Embeddings

Computers don't understand meaning; they understand numbers. To search by "meaning" (semantics) and not just keywords, we use Embeddings.
An embedding model (like text-embedding-3-small) turns a sentence into a list of 1,536 numbers (a vector).

• "Dog": [0.1, 0.9, 0.3, ...]
• "Puppy": [0.12, 0.88, 0.31, ...] (Mathematically close!)
• "Car": [0.9, 0.1, 0.1, ...] (Mathematically far away).

We store your documents as vectors. When a user searches, we convert their question to a vector and use Cosine Similarity to find the closest matches.

• Benefit: Searching for "Vacation" successfully finds "Holiday Policy" even if the word "vacation" is never used.

Advanced RAG Strategies (What Agencies Do)

Basic RAG is easy (send top 3 chunks). Advanced RAG is where the quality is.

1. Hybrid Search

Vector search is bad at exact matches (like Product SKU #AB-123).

• Solution: Combine Vector Search + Keyword Search (BM25). Rank results using Reciprocal Rank Fusion (RRF).

2. Metadata Filtering

• Problem: Searching "My Salary" might return the CEO's salary if you aren't careful.
• Solution: Apply filters at the database level: WHERE user_id = current_user OR doc_type = 'public'.

3. Re-Ranking

• Process: Retrieve 50 documents from the DB (fast). Then use a "Re-Ranker" model (like Cohere) to carefully sort them by relevance and send only the top 5 to the LLM (slow but accurate).

The Tech Stack

At Panoramic Software, our standard RAG stack is:

• Vector DB: Pinecone (Managed) or Supabase (pgvector).
• Orchestration: LangChain.
• Embedding Model: OpenAI text-embedding-3-small (Cheap & Good).

RAG is the bridge between your proprietary value and generic AI intelligence.