What is RAG? Fundamental Definitions and Concepts
RAG (Retrieval-Augmented Generation) is an AI architecture that combines intelligent search with response generation. Unlike generic ChatGPT which only "remembers" training information, RAG:
- Searches relevant documents in your database
- Extracts the most pertinent fragment
- Generates a response citing the exact source
You might also like: Semantic Indexing: AI-Powered Intelligent Search System and ChatGPT vs IgeraFincas: Why RAG Beats Generic AI Systems
Key Components of a RAG Architecture
1. Document Ingestion (Data Layer)
PDFs and documents are imported, converted to text, split into intelligent chunks, and prepared for indexing. This step is critical: bad chunking = bad searches.
2. Embeddings (Vector Representation)
Each fragment is converted into a numerical vector representing its semantic meaning. This enables searches by "sense", not just keywords.
3. Vector Database (pgvector in Supabase)
Vectors are stored in a specialized database that enables ultra-fast similarity searches. Igera uses pgvector with IVFFlat indexes for maximum performance.
4. LLM Orchestration (Gemini 2.0 Flash)
Once relevant documents are retrieved, a powerful LLM generates the final response, citing exactly where each piece of data came from.
RAG vs Generic LLMs: Why RAG Wins
Problem with ChatGPT: "What is article 17.2 of the LPH?" → Can give a correct-sounding answer but made it up (hallucination).
Solution with RAG: Automatically searches for the real article in your database, extracts it word-for-word, and gives you a 100% verified answer with citation to the original PDF.
Implementation in Igera Solutions
Igera is built on three layers of RAG:
- Public Layer: LPH + European legislation preloaded (without the admin uploading anything)
- Community Layer: Bylaws, internal regulations and documents specific to each community
- Operational Layer: Contracts, budgets, and community decision history
Ready to Implement RAG?
Igera Solutions already has everything configured. You just need to:
- Upload your Bylaws and internal regulations (PDF or DOCX)
- Let the system index (3-5 minutes per document)
- Ask questions and get 100% verified answers
No technical knowledge required. RAG works in the background, automatically.
Related Articles
- Semantic Indexing: AI-Powered Intelligent Search System
- ChatGPT vs IgeraFincas: Why RAG Beats Generic AI Systems
- RAG vs Traditional Search: Why Search Engines Aren
- How to Implement RAG in Your Company: Complete Guide with Real Use Cases
- Why ChatGPT Fails with Legal Community Documents & Rules
Discover how IgeraFincas answers residents' questions by citing the exact clause from the bylaws, without the manager having to intervene.
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