We’ve all seen the hype around Retrieval-Augmented Generation (RAG), but the real test is whether it actually solves problems inside an enterprise.
Example:
A technician visit gets canceled because a chatbot said it was fine—except it wasn’t.
Why?
The model didn’t know about a business rule added after it was trained.
No retrieval = confident wrong answer.
That’s the problem RAG is meant to solve:
Pulling live, policy-aware, domain-specific info into the generation process—without retraining the model.
Over the last few months, we’ve seen RAG systems go from keyword-matching all the way to:
- Semantic and hybrid retrieval
- Agentic RAG that decides what to fetch and how to use it
- Graph-based systems connecting multi-step logic
- Use across CRMs, ERPs, HRMS, legal, support desks, and more
The tech is impressive, but the challenges are real:
- Bad chunking
- Irrelevant retrieval
- Latency under load
- Sensitive data leaks
- Response hallucination when context goes stale
We’re curious how others here are thinking about it:
- Have you tried building or using a RAG pipeline in production?
- What’s worked well (or not) in your use case?
- Do you see this as a short-term fix or long-term foundation?
If you’re interested, we recently mapped out how RAG has evolved and what it takes to implement it safely and effectively across business systems.
Happy to share more if there’s interest.
https://nalashaadigital.com/blog/a-guide-to-rag-in-ai/
Would love to hear your perspective, especially if you’re experimenting with LLMs in real workflows.
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