Dear IT departments, please stop trying to build your own RAGs

Think about it, most of us businesses aren't going to build our own CRM systems or custom ERP—or, in most cases, our own LLM.

Can you?

Yet I see IT departments everywhere convincing leaders that building their own RAG-based chat tool will be different. It’s not. Mostly, it’s worse.

Let me paint a picture for you: Last week, I watched a team of very skilled engineers demo their shiny new RAG pipeline. Developed entirely in-house. They were proud. They were excited. They had vector embedding! They had fast engineering! They had… no idea what was coming next.

Trust me, I’ve seen this movie before. Multiple times. The ending is always the same: engineers burn out, budgets are wasted, and the CTO wonders why they didn’t just buy the solution in the first place.

A "Looks Simple" Trap

I get it. Really, I get it. You look at RAG and think:

“Vector DB + LLM = Done!”

Throw in some open source tools, maybe some Langchain or DeepSeek, and you’re good to go, right?

Wrong. Very wrong.

Let me tell you about a mid-sized company I recently interviewed. Their “simple” RAG project started in January. By March, they had:

• 1 full-time engineer debugging illusion and accuracy issues.

• 1 full-time data person is responsible for handling ETL and extraction issues.

• 1 full-time DevOps engineer is working on scalability and infrastructure issues.

• One CTO was very unhappy when he saw his budget tripled.

The worst part was watching them slowly realize that this project that was supposed to last only two months was actually going to be an ongoing nightmare .

Here are some things they fail to take into account:

• Complexity of document and knowledge base pre-processing (trying to extract various data sources like Sharepoint, websites)

• Document formatting and various PDF issues (or trying to import an epub)

• Accuracy issues in production (everything works great in testing, but horrible in production use in front of real users!)

• Hallucination!

• Response Quality Assurance

• Integrate with existing systems

• Change data capture (e.g. if data on the website changes, is the RAG in sync?)

• Compliance and audit requirements

• Security issues and data breaches (Are your internal systems SOC-2 Type 2 compliant?)

Each of these could be its own project. Each has its own pitfalls. Each could throw off your schedule.

2. Costs No One Talks About

"We have the talent! We have the tools! Open source is free!"

Stop! Stop! Stop!

Let me break down the actual cost of a “free” RAG system:

Infrastructure costs

• Vector Database Hosting

• Model inference cost

• Development Environment

• Test environment

• Production Environment

• Backup System

• Monitoring system

Personnel costs

• Machine Learning Engineer (Annual Salary 150,000-250,000 RMB)

• DevOps Engineer (annual salary 120,000-180,000)

• Artificial Intelligence Security Expert (Annual Salary 160,000-220,000 RMB)

• Quality Assurance (90,000 - 130,000 per year)

• Project Manager (100,000-200,000 per year)

Ongoing operating costs

• 24/7 Monitoring

• Security Updates

• Model Upgrade

• Data cleaning

• Performance Optimization

• Documentation Updates

• New team member training

• Compliance Audit

• Feature parity (as AI advances)

Here’s the problem: while you’re burning money to build all of this, your competitors are already in production with solutions they purchased at a fraction of the cost.

You may ask why?

Because the solution you purchased has been tested among thousands of customers. And the cost of building it has also been amortized among thousands of customers. In your case, the entire time + expense cost is covered.

Three security nightmares

Want to keep you awake at night? Try being in charge of an artificial intelligence system:

• Gain access to your company's entire knowledge base

• Sensitive information may be disclosed

• May create the illusion of confidential data

• Requires constant security updates

• May be vulnerable to rapid injection attacks

• Possibly expose internal data via model responses

• May be vulnerable to adversarial attacks

I recently spoke to a CISO who discovered that their internal RAG system was accidentally leaking internal document titles through its responses. It was interesting. They spent three weeks fixing the issue. Then they found five more similar issues.

Guess what? Threats are evolving faster than your team can keep up. Last month’s security measures may be outdated today. The attack surface is expanding, and the bad guys are getting more sophisticated.

Think about it: Every new document you add to your knowledge base is a potential security risk. Every tip is an attack vector. Every response requires screening. It's not just about building a secure system — it's about maintaining security in an environment that changes every day.

The Terror of Four Maintenance

Here’s what happened next:

• Week 1: Everything goes well

• Week 2: Latency Issues

• Week 3: Weird edge cases

• Week 4: Complete Rewrite

• Week 5: New hallucination problems

• Week 6: New data extraction project.

• Week 7: Vector DB Migration and Performance Issues

• Week 8: Rewrite Again

These things are not unique to the companies listed above. This is the typical life cycle of an internal RAG system. And maintenance will generate many tasks:

Daily maintenance tasks

• Monitor response quality

• Check for hallucinations

• Debugging edge cases

• Address data processing issues.

• Manage API quotas and infrastructure issues.

Weekly maintenance tasks

• Performance Optimization

• Security Audit

• Data quality check

• User feedback analysis

• System Update

Monthly maintenance tasks

• Large-scale testing

• AI model updates.

• Compliance Review

• Cost Optimization

• Capacity Planning

• Architectural Review

• Strategy coordination

• Feature requests.

All of this needs to happen while you’re trying to add new features, support new use cases, and keep your business running smoothly.

Five expertise gaps

“We have great engineers!”

Of course. But RAG is more than just engineering. Let me break down what you really need:

Machine Learning Operations

• LLM Model Deployment Expertise

• RAG Pipeline Management

• Version control of models

• Accuracy optimization

• Resource Management

• Expand your knowledge

RAG Expertise

• Understanding Accuracy

• Anti-hallucination optimization

• Context window optimization.

• Understand the delays and costs.

• Just in time engineering

• Quality indicators

Infrastructure knowledge

• Vector database optimization

• Logging and monitoring.

• API Management

• Cost Optimization

• Extended Architecture

Safety expertise

• AI-specific safety measures

• Timely preventive injection

• Data Privacy Management

• Access Control

• Audit log

• Compliance Management

In this market, recruiting talent is a difficult thing. Even if you can find these people, can you afford them? Can you keep them? Because every other company is also looking for the same talent.

More importantly: As other RAG platforms continue to improve their services and add more features and better KPIs (like accuracy and hallucination prevention), will your RAG team do the same? In the next 20 years?

Six official operation time reality

When building a RAG system:

• Your competitors are deploying production solutions

• Technology is constantly evolving (sometimes weekly)

• Your requirements are changing

• Your business is missing out on opportunities

• The market is moving forward

• Your initial design is outdated

• Users' expectations are increasing.

Let's discuss a realistic timeline for building a production-ready RAG system:

Month 1: Initial Development

• Basic Architecture

• First prototype

• Initial examination

• Early Feedback

Month 2: Reality hits

• Security issues emerge

• Performance issues emerge

• More edge cases

• Changing requirements

Month 3: Rebuilding

• Schema revision

• Security improvements

• Performance Optimization

• Documentation Catch-up

Month 4: Business Readiness

• Compliance Implementation

• Monitoring settings

• Disaster Recovery

• User Training

That's if everything goes well. But it doesn't. Just wait to go into production!

Seven alternatives

I'm not saying never build. I'm saying choose wisely what to build and why.

Modern RAG solutions offer:

Infrastructure Management

• Scalable architecture

• Automatic Updates

• Performance Optimization

• Security maintenance

Enterprise Features

• Role-based access control

• Audit log

• Compliance Management

• Data Privacy Controls

Operational benefits

• Expert Support

• Regular updates

• Security patches

• Performance Monitoring

Business Advantages

• Accelerate time to market

• Reduce total cost

• Reduce risk

• Proven Solutions

When is it appropriate to build?

There are three situations suitable for construction:

1. You have truly unique regulatory requirements that no supplier can meet

• Customized government regulations

• Specific industry compliance requirements

• Unique security protocol

2. You are building RAG as your core product

• This is your main value proposition

• You are innovating in this area

• You have deep expertise

3. You have unlimited time and money (if this is you, call me)

• But honestly, that doesn't exist.

• Even with resources, opportunity cost matters

• Time to market still matters

8 You should do this

1. Focus on your actual business problem

• What do your users actually want to achieve?

• What is your unique value proposition?

• Where can you have the greatest impact?

2. Choose a reliable RAG provider

• Evaluate based on your needs (hint: check out the case studies)

• Check security credentials (hint: check SOC-2 Type 2)

• Validate enterprise readiness (hint: ask for case studies!)

• Test performance (hint: check published benchmarks)

• Check the quality of support (hint: call support!)

3. Spend your engineering time on things that will truly differentiate your business

• Custom Integration

• Unique Features

• Business Logic

• User Experience

Because here’s the thing: Five years from now, no one is going to care whether you built or bought a RAG system. They’re just going to care whether their pain point was solved.

summary

Stop trying to reinvent the wheel. Especially when that wheel is actually a complex, AI-driven spacecraft that requires constant maintenance and can explode if you get the details wrong.

Building your own RAG system is like deciding to build your own email server in the year 2025. Sure, you can do it. But why would you? The most important thing is to actually solve real problems instead of debugging accuracy issues at 3am. The choice is yours. But choose wisely.