18 months ago, I thought I was ready to build AI systems.
I'd been a backend engineer for years. I'd shipped production systems. I knew databases, APIs, authentication, and queues, and I have built and shipped production backend systems.
When my team needed to add AI features by creating a Mock interview platform for backend engineers, I volunteered.
I said to myself, “ How hard could it be?”
I'd watched the tutorials. I understood embeddings, vector databases, RAG, and agents. I'd built demos that worked perfectly on my laptop.
Two weeks after we shipped to production, everything broke.
Here's what went wrong:
The first failure was expensive.
I'd built an AI agent to help our users with a mock interview platform, so they can practice their interview before the real interview using AI. It worked beautifully in testing. Handled edge cases gracefully and impressed everyone in the demo.
In production, one edge case sent it into a loop.
A user asked a question that the agent couldn't fully answer. Instead of gracefully failing, it kept trying. And trying. And trying. Each attempt was another API call. Each API call costs money.
I woke up to $400 in charges and a very long log file.
The fix was simple. It was to add iteration limits, cost ceilings, and timeout enforcement. Basic guardrails that any production system should have.
But no tutorial had mentioned them. Every agent demo I'd watched showed the happy path: “user asks a question, agent answers brilliantly”, everyone claps.
Nobody showed what happens when the agent doesn't know when to stop.
The next problem I faced was the case of hallucinations.
Then came the hallucinations.
The second failure was public.
We'd built a RAG system to answer questions using our company's job description for the user. In testing, it was impressive. Users could ask natural language questions and get accurate answers pulled from our docs.
Then a user asked about our salary expectations in one of the interviews.
The AI confidently cited a salary expectation that didn't exist. It had hallucinated a specific, detailed salary expectation that was never publicly written by the company.
The user screenshot it and posted it on Twitter.
I spent three days debugging. The problem wasn't the model. It was my retrieval pipeline.
I was returning chunks based purely on semantic similarity. But semantic similarity doesn't mean relevance. The chunks I was retrieving were related to salary expectations. The job description that the user added did not mention any remuneration.
The model was doing its best with bad context. Garbage in, garbage out.
The fix required rethinking my entire chunking strategy, adding a reranking layer, and implementing relevance thresholds. None of which I'd seen in any tutorial.
The Memory Leak
The third failure was architectural.
Our conversation memory system worked great for short chats. Users could have back-and-forth conversations, and the AI would remember context from earlier in the conversation.
Then, power users discovered it.
Some users had conversation histories with longer messages. Every time they sent a new message, we loaded their entire history into context.
The service started crashing during peak hours. Memory usage spiked. Response times ballooned.
I'd stored every message without thinking about:
Context window limits
Summarization strategies
Memory management for long conversations
Graceful degradation when history is too long
Another week of firefighting. Another lesson I should have learned before production.
What I Realized
Every tutorial I'd watched was useless in building real production-ready AI systems. They showed happy-path demos that fell apart the moment real users touched them.
Not because the instructors were bad. Because they'd never built AI in production either.
They were teaching concepts, not systems. They showed how to call APIs, not how to build infrastructure. They demonstrated happy paths, not failure modes.
I needed something different. I needed to learn how to build AI as infrastructure, with the same discipline I applied to databases, queues, and APIs.
So I threw out everything and rebuilt from first principles.
What I Learned
Over the next several months, I developed a framework for thinking about AI backends. Here's what I discovered:
RAG isn't magic, it's a simple retrieval architecture
Once you understand RAG as a retrieval system, you can debug it systematically.
When you have bad results, then you know you need to check your chunking strategy or check your embedding model. You can also check your similarity thresholds. Add reranking and implement relevance filtering.
Each failure mode has specific symptoms and specific solutions. It's not magic. It's engineering.
Agents need guardrails, not suggestions
Some tutorials will show you an agents that is already working or a simple AI Agent that just respond to messages.
However, that’s not entirely true for building complex and sophisticated AI-powered AI agents. It requires work and the use of the first principle of building backend systems.
Production agents need:
Iteration limits: So they don't run forever
Cost ceilings: So they don't drain your budget
Timeout enforcement: So users don't wait forever
Explicit allowed actions: So they don't do dangerous things
Human approval for high-stakes decisions: Because AI shouldn't be fully autonomous
These aren't optional. They're requirements.
Embeddings aren't understanding
Embeddings are mathematical projections into a vector space. They capture semantic similarity, not meaning.
Two sentences can be semantically similar but contextually irrelevant. Understanding this limitation is how you build systems that don't hallucinate at scale.
AI observability is different
Traditional logging captures what your code did. AI observability needs to capture what the model did and why.
You need to trace:
What prompt was sent
What context was retrieved
What the model returned
How long each step took
How much does each step cost
Without this, debugging AI failures is guesswork.
Production AI is backend engineering
This was the biggest insight.
The skills that make you good at building reliable backend systems, such as error handling, caching, cost management, observability, and graceful degradation, are exactly what AI systems need.
If you’ve been treating AI as something separate from backend engineering. It's not. It's a new domain that needs the same discipline.
Let’s explore the 6 layers of Production-Ready AI Backend Engineering.
The 6-Layer Framework
From these lessons, I developed a framework I now use for every AI backend:
Layer 1: Foundation Authentication, database schema, input validation, environment configuration. The backend basics that everything else depends on.
Layer 2: Business Logic RBAC, background jobs, integrations, workflow logic. Real systems that companies actually use, not toy demos.
Layer 3: Production Hardening, Caching, security, monitoring, observability. The infrastructure that makes systems survive real traffic.
Layer 4: AI Infrastructure Vector databases, embeddings, RAG pipelines, AI agents with proper guardrails. AI is built on solid backend foundations.
Layer 5: AI Systems Human-in-the-loop workflows, conversation memory, AI-specific observability, hallucination detection. The systems that make AI safe and maintainable.
Layer 6: Defense The ability to explain every decision, debug any failure, and justify every trade-off. Proving you understand what you built.
Each layer builds on the ones below it. Skip a layer, and your system is fragile. I wrote a comprehensive article on The 6 Layers Every AI Backend Needs
Why I Built the Bootcamp
I built this bootcamp because I wish it existed when I started.
I wish someone had said: "Here's how AI systems actually break in production. Here's how you prevent it. Here's how you debug it when it happens anyway."
I wish someone had taught me the 6-layer framework before I learned it through painful experience.
So I'm packaging everything I learned into a 6-week program.
This is not another theory-filled bootcamp. It’s a practical-based production-ready AI backend engineering bootcamp that will help you build production systems.
You'll build each layer in order. You'll ship code every week. And at the end, you'll defend your system in a live evaluation, proving you actually understand what you built.
What's Next
If you're reading this, you're probably considering the bootcamp.
Here's what I'd suggest:
If you want to see how I teach: Join the free workshop. I'm teaching the 6-layer framework live. No cost, no commitment. You'll know within 90 minutes whether my teaching style works for you.
If you're ready to build: Join the waitlist at masteringai.dev. Early bird enrollment opens after the workshop. 50 spots, first cohort.
If you have questions: Email me at hi[at]masteringbackend.com. I read everything. I'll answer.
The AI transition is happening. The engineers who can build AI infrastructure, and not just use AI tools, will lead it.
This bootcamp is how you get there.
