Is RAG the same as "fine-tuning" an AI model on my documents?

No — they are fundamentally different approaches. Fine-tuning (also called re-training) bakes the patterns from your documents permanently into the model's weights — the internal numerical parameters that determine how the model behaves. This is expensive, slow, and means you need to re-fine-tune every time your documents change. RAG does not modify the model at all. Instead, it retrieves relevant passages from your current documents at query time and feeds them to the model as context. RAG is faster to set up, cheaper to maintain, inherently up-to-date, and produces traceable outputs. For most business document Q&A applications, RAG outperforms fine-tuning on both accuracy and operational simplicity.

Can RAG handle scanned PDFs or only digital text documents?

A complete RAG pipeline includes an OCR (Optical Character Recognition) step that extracts text from scanned images before indexing. Quality varies: a clean, high-resolution scan of a typed lease will be indexed accurately; a faded photocopy of a handwritten amendment will produce poorer results. When evaluating a RAG product, ask specifically about its document ingestion pipeline — does it include OCR, how does it handle mixed-format documents (PDFs with both text and scanned pages), and does it flag low-confidence extractions? IgeraFincas processes both digital PDFs and scanned documents and alerts the property manager when OCR confidence is below threshold.

Does using RAG mean the AI is always right?

No. RAG eliminates hallucinations that come from a lack of grounding in your documents, but it does not make the AI infallible. Three failure modes remain. First: if your uploaded document contains an error (a typo in a clause number, an outdated version), the system will faithfully reproduce that error. Second: the retrieval step may surface the wrong passage if the question is ambiguous or the document structure is unusual — in which case the answer is confidently wrong rather than hallucinated. Third: complex legal questions often require interpretation, not just quotation — and interpretation requires human judgement. RAG is a precision tool for factual document Q&A. It does not replace professional advice on ambiguous or contested matters.

How long does it take to set up a RAG system for a property management office?

With a purpose-built platform like IgeraFincas, setup for a typical property management office — 20 to 50 communities, each with a standard set of documents — can be completed in under a week. The main tasks are: upload the documents for each community, confirm that OCR extraction looks correct, and test a sample of queries against known answers from those documents. The platform handles indexing automatically. The time-consuming part is gathering and organising the documents themselves, not the technical configuration. For offices with well-organised document archives, meaningful question-answering capability can be available on day one.

IA General

IA con RAG explicada para directivos sin perfil técnico: qué es y por qué importa

Equip IgeraSolutions

6 de junio de 2026

10 min read

RAG AI explained for non-technical business managers — retrieval-augmented generation in plain language

#RAG AI explained non-technical#retrieval augmented generation business managers#RAG vs ChatGPT difference#AI hallucination prevention RAG#RAG property management AI#what is RAG AI simple explanation#RAG document Q&A business#AI vendor evaluation RAG#IgeraFincas RAG explanation#generative AI without hallucinations business

IA con RAG explicada para directivos sin perfil técnico: qué es y por qué importa

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