Don't Let Your AI Project Fail Before It Starts - Why "Retrieval" Is the Real MVP in RAG

When companies dive into building AI-powered solutions using something called Retrieval-Augmented Generation (RAG), the spotlight usually goes to the flashy stuff: large language models (LLMs), generative AI, and cool demos. But behind the scenes, there's a quiet killer of momentum, budgets, and user trust:

The retrieval layer.

This is the part of the system that actually goes out and finds the information the AI needs to generate a useful response. And when it doesn’t work well, everything else falls apart—fast.

The Missing Link in RAG Projects

RAG is a fancy term for combining search with AI. It promises smarter answers by giving AI models access to up-to-date, internal knowledge. But most teams focus only on the AI part and ignore how that information gets retrieved in the first place.

That’s where projects hit the wall.

At MC+A, we’ve spent over 20 years helping companies fix and modernize their search systems. And lately, we’ve been called into a lot of AI projects that are stuck—projects where teams can’t figure out why their AI isn’t scaling, why it’s slow, or why it’s so expensive to run.

The common thread? Retrieval was treated as an afterthought.

3 Myths That Sabotage AI Projects

Here are three assumptions that quietly ruin AI pilots before they even launch:

1. “Just add more hardware and it’ll scale.” - It’s not about more power—it’s about better architecture.
2. “Throw everything into the AI and it’ll figure it out.” - Nope. If you feed the AI a mess, you’ll get messy answers.
3. “The database can handle it.” - Not without a retrieval strategy built for AI—it’s a different beast.

Not without a retrieval strategy built for AI—it’s a different beast.

Warning Signs You Have a Retrieval Problem

Here’s how you know the retrieval layer is holding your project back:

• Searches are slow or inconsistent
• The AI returns irrelevant or incomplete answers
• Cloud costs are climbing fast
• Users get frustrated—and stop trusting the system

These aren’t just technical issues—they’re business risks. Poor retrieval kills user confidence, wrecks adoption, and leads to expensive rework.

Real Talk: It’s Not the AI That’s Slow—It’s the Search

Let’s look at a real example. A client came to us and said:

“We'd like to know how much hardware we need to support 1000 concurrent users” - Client

“10 times as much hardware as you got” - MC+A Consultant

What Went Wrong: A Classic "Oversharding" Mistake

In systems like Elasticsearch (a popular search engine used in RAG), data is broken into small pieces called shards. This helps spread out the work—but too many shards = big trouble.

In this case, every search was hitting 500+ shards. That means every single search triggered 500 separate mini-searches, which all needed computing power. Now imagine what happens when two users search at once. Or 1,000. You get the picture: long delays, jammed systems, and very frustrated users.

The client had followed “best practices” for setting up their system—but didn’t fully understand why those practices existed or how they applied in an AI context.

The Bottom Line

If your RAG system is slow, expensive, or stuck in pilot mode, the problem probably isn’t your AI model. It’s your retrieval layer.

Get that right, and everything else works better:

• Faster responses
• "Smarter" Generative AI
• Lower costs
• Happier users

Bonus: Want to Check Your System’s Readiness?

We’ve put together a quick sizing checklist (yep, a simple spreadsheet) to help you avoid the common pitfalls and set your RAG project up for success.