"Isn't RAG simple? Just connect OpenAI with Pinecone..." If it were like that, no team/AI builder would waste weeks implementing it... Who suffers more than they should because of this?

Have you ever tried to break a document into chunks and ended up losing the meaning of the text? The chunking process is critical in RAG: if done wrong, the AI ​​responds out of context. If done right, it connects information seamlessly.

How to ensure relevance without slowing down latency? This is the dilemma of any RAG system. Common strategies: re-ranking with LLM, intelligent caching, or contextual filters before querying the vector database.

Security isn't optional in RAG. Imagine a collaborator accessing documents they shouldn't have, simply because the retrieval didn't respect permissions. The challenge is to apply access control at the chunk level, ensuring that the AI ​​only uses what the user can actually see.

Keyword or semantics? That is the question. In RAG, the strategy can be purely semantic (embeddings) or hybrid.

Your AI gave a wrong answer. How do you know where the error is? Without observability, you can't know if the error came from retrieval, embedding, or LLM Mature companies are creating RAG observability pipelines, measuring metrics like recall, precision, hit rate, and coverage

Documents today aren't just text. We're processing tables, images, and audio files. In RAG, chunking scanned PDFs or reports full of graphs is a common nightmare. Solutions: robust OCR, multimedia preprocessing, and data-type-specific embeddings.

Is it worth repeating sections between chunks to preserve context? Overlapping can prevent information loss at paragraph breaks, but it increases storage costs and latency. But there are cases where excess redundancy hinders more than it helps.

When to chunk: at ingest time or at query time? With ingest, you ensure consistency and query speed. But you can pay dearly for outdated data. At query time, you have flexibility… but add latency. Major players are combining both approaches in hybrid pipelines.

Names, dates, amounts. It seems simple, but if chunking breaks them in half, the LLM can get lost. I've seen RAG systems give absurd responses because a contract number was split in half. Ultimately, it's the details that make or break user confidence.

What if the model itself defined where to cut? Semantic segmentation uses embeddings to decide breakpoints, respecting meaning and flow. It works well on long texts, but can be more expensive than fixed windows. In sensitive domains, the cost can be worth every penny.

Closing the year with gratitude. Building what’s next with intention - and context.

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