Dear IT Departments, Please Stop Trying To Build Your Own RAG

Author(s): Alden Do Rosario

Originally published on Towards AI.

Look:

You would never ever in a million years build your own CRM system or custom CMS — or in most cases, your own LLM.

Would you?

And yet, everywhere I look, I see IT departments convincing themselves that building their own RAG-based chat is somehow different. It’s not. It’s actually worse.

Let me paint you a picture: Last week, I watched a team of brilliant engineers demo their shiny new RAG pipeline. All built in-house. They were proud. They were excited. They had vector embeddings! They had prompt engineering! They had… no idea what was coming.

And trust me, I’ve seen this movie before. Multiple times. It always ends the same way: with burned-out engineers, blown budgets, and a CTO wondering why they didn’t just buy a solution in the first place.

The “It Looks Simple” Trap

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

“Vector DB + LLM = Done!”

Throw in some open source tools, maybe some Langchain (oh, we’ll get to that), and you’re good to go, right?

Wrong. So, so wrong.

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

• 1 full-time engineer debugging hallucinations and accuracy problems.
• 1 full-time data guy dealing with ETL and ingestion problems.
• 1 full-time DevOps engineer fighting with scalability and infrastructure issues.
• 1 very unhappy CTO looking at a budget that had tripled.

And that’s not even the worst part. The worst part was watching them slowly realize that what looked like a two-month project was actually going to become an ongoing nightmare.

Here’s are some of the things that they didn’t account for:

• Document and knowledge base pre-processing complexity (try ingesting various data sources like Sharepoint, Google Drive and websites)
• Document formats and all sorts of PDF issues (or try importing epub)
• Accuracy issues in production (Oh wait — everything worked well in testing, but production usage in front of actual users sucks!)
• Hallucinations!
• Response quality assurance
• Integration with existing systems
• Change-data-capture (e.g. data on website changes, does the RAG remain in sync?)
• Compliance and audit requirements
• Security issues and data leakages (is your internal system going to be SOC-2 Type 2 compliant?)

Each one of these could be its own project. Each one has its own gotchas. Each one can blow up your timeline.

The Cost Nobody Talks About

“But we have the talent! We have the tools! Open source is free!”

Stop. Just stop.

Let me break down the real costs of your “free” RAG system:

Infrastructure Costs

• Vector database hosting
• Model inference costs
• Development environments
• Testing environments
• Production environments
• Backup systems
• Monitoring systems

Personnel Costs

• ML Engineers ($150k-$250k/year)
• DevOps Engineers ($120k-$180k/year)
• AI Security Specialists ($160k-$220k/year)
• Quality Assurance ($90k-$130k/year)
• Project Manager ($100k-$200k/year)

Ongoing Operational Costs

• 24/7 monitoring
• Security updates
• Model upgrades
• Data cleaning
• Performance optimization
• Documentation updates
• Training for new team members
• Compliance audits
• Feature parity (as AI evolves)

And here’s the kicker: while you’re burning cash building all this, your competitors are already in production with their bought solution, spending a fraction of the cost.

Why you ask?

Because the purchased solution has been tested across thousands of customers. And the cost of building it too has been amortized across thousands of customers. In your case, the entire time + money cost has been “Divided by One”

The Security Nightmare

Want to lose sleep? Try being responsible for an AI system that:

• Has access to your company’s entire knowledge base
• Could potentially leak sensitive information
• Might hallucinate confidential data
• Needs constant security updates
• Could be vulnerable to prompt injection attacks
• Might expose internal data through model responses
• Could be susceptible to adversarial attacks

I recently spoke with a CISO who discovered their in-house RAG system was accidentally leaking internal document titles through its responses. Fun times. They spent three weeks fixing that one. Then they found five more similar issues.

And guess what? The threats evolve faster than your team can keep up with. Last month’s security measures might be obsolete today. The attack surface is constantly expanding, and the bad actors are getting more sophisticated.

Consider this: every new document you add to your knowledge base is a potential security risk. Every prompt is an attack vector. Every response needs to be screened. It’s not just about building a secure system — it’s about maintaining security in an environment that changes daily.

The Maintenance Horror Show

Remember that startup I mentioned that launched with Langchain? Here’s what happened next:

• Week 1: Everything works great
• Week 2: Latency issues
• Week 3: Weird edge cases
• Week 4: Complete rewrite
• Week 5: New hallucination issues
• Week 6: New data ingestion project.
• Week 7: Vector DB migration and performance problems
• Week 8: Another rewrite

They’re not alone. This is the typical lifecycle of an in-house RAG system. And it gets better (worse):

Daily Maintenance Tasks

• Monitoring response quality
• Checking for hallucinations
• Debugging edge cases
• Handling data processing issues.
• Managing API quotas and infrastructure issues.

Weekly Maintenance Tasks

• Performance optimization
• Security audits
• Data quality checks
• User feedback analysis
• System updates

Monthly Maintenance Tasks

• Large-scale testing
• AI model updates.
• Compliance reviews
• Cost optimization
• Capacity planning
• Architecture reviews
• Strategy alignment
• Feature requests.

And all of this needs to happen while you’re also trying to add new features, support new use cases, and keep the business happy.

The Expertise Gap

“But we have great engineers!”

Sure you do. But RAG isn’t just engineering. Let me break down what you actually need:

ML Operations

• LLM Model deployment expertise
• RAG pipeline management
• Version control for models
• Accuracy optimization
• Resource management
• Scaling knowledge

RAG Expertise

• Understanding accuracy
• Anti-hallucination optimization
• Context window optimization.
• Understanding latency and costs.
• Prompt engineering
• Quality metrics

Infrastructure Knowledge

• Vector database optimization
• Logging and monitoring.
• API management
• Cost optimization
• Scaling architecture

Security Expertise

• AI-specific security measures
• Prompt injection prevention
• Data privacy management
• Access control
• Audit logging
• Compliance management

And good luck hiring for all that in this market. 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.

And more importantly: As other RAG platforms continue to improve their service and add more features and better KPIs like accuracy and anti-hallucination, will your RAG team do the same? Over the next 20 years?

The Time-to-Market Reality

While you’re building your RAG system:

• Your competitors are deploying production solutions
• The technology is evolving (sometimes weekly)
• Your requirements are changing
• Your business is losing opportunities
• The market is moving forward
• Your initial design is becoming obsolete
• User expectations (tempered by OpenAI) are increasing daily.

Let’s talk about a real timeline for building a production-ready RAG system:

Month 1: Initial Development

• Basic architecture
• First prototype
• Initial testing
• Early feedback

Month 2: Reality Hits

• Security issues emerge
• Performance problems surface
• Edge cases multiply
• Requirements change

Month 3: The Rebuild

• Architecture revisions
• Security improvements
• Performance optimization
• Documentation catch-up

Month 4: Enterprise Readiness

• Compliance implementation
• Monitoring setup
• Disaster recovery
• User training

And that’s if everything goes well. Which it won’t. Just wait till things hit production!

The Buy Alternative

Look, I’m not saying never build. I’m saying be smart about what AND why you are building.

Modern RAG solutions provide:

Infrastructure Management

• Scalable architecture
• Automatic updates
• Performance optimization
• Security maintenance

Enterprise Features

• Role-based access control
• Audit logging
• Compliance management
• Data privacy controls

Operational Benefits

• Expert support
• Regular updates
• Security patches
• Performance monitoring

Business Advantages

• Faster time-to-market
• Lower total cost
• Reduced risk
• Proven solutions

When Should You Build?

Okay, fine. There are exactly three scenarios where building makes sense:

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

• Custom government regulations
• Specific industry compliance needs
• Unique security protocols

2. You’re building RAG as your core product

• It’s your main value proposition
• You’re innovating in the space
• You have deep expertise

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

• Seriously, though, this doesn’t exist
• Even with resources, opportunity cost matters
• Time-to-market still matters

Here’s What You Should Do Instead

1. Focus on your actual business problems

• What are your users actually trying to achieve?
• What are your unique value propositions?
• Where can you make the biggest impact?

2. Choose a reliable RAG provider

• Evaluate based on your needs (Hint: Look at case studies)
• Check security credentials (Hint: Check for SOC-2 Type 2)
• Verify enterprise readiness (Hint: Ask for case studies!)
• Test performance (Hint: Look at published benchmarks)
• Check support quality (Hint: Call support!)

3. Spend your engineering time on things that actually differentiate your business

• Custom integrations
• Unique features
• Business logic
• User experience

Because here’s the truth: In five years, no one will care if you built or bought your RAG system. They’ll only care if their pain point is solved.

The Bottom Line

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

Building your own RAG system is like deciding to build your own email server in 2024. Sure, you could do it. But why would you want to?

Your future self will thank you. Your engineers will thank you. Your budget will thank you.

And most importantly, your business will thank you when you’re actually solving real problems instead of debugging accuracy problems at 3 AM.

The choice is yours. But please, choose wisely.

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Published via Towards AI