Solomon Eseme

Hey everyone 👋

I'm hosting a free live workshop tomorrow (Friday) on building production AI backends.

"The 6 Layers Every AI Backend Needs"

If you've tried building AI features and run into issues like:

- Hallucinations you couldn't debug
- Costs that spiraled unexpectedly
- RAG that returned irrelevant results

This workshop explains why and how to fix it.

90 minutes. Live demo. Q&A. Free.

Register: luma.com/3jcfa4v0

Happy to answer any questions here!

6 days ago | [YT] | 0

Solomon Eseme

Most backend engineers will miss the opportunity to migrate their careers with AI.

Not because they're bad engineers.

But because they're learning AI the wrong way.

Here's what I mean ↓

You've probably:

- Taken a prompt engineering course
- Built a ChatGPT wrapper side project
- Added "AI/ML" to your LinkedIn headline
- Watched tutorials on LangChain and vector databases

And you still can't build AI systems in production.

That's not your problem. That's an industry problem.

The AI education market sold you a lie:

"Learn to talk to AI better, and you'll be valuable."

But companies don't need people who talk to AI.

They need engineers who BUILD the infrastructure that makes AI work.
RAG pipelines. AI agents. Memory systems. Human-in-the-loop. Cost controls. Observability.

That's backend engineering. Not prompting.

I learned this the hard way.

18 months ago, I shipped my first AI feature in production.

Within 2 weeks:

- $400 API bill from runaway agents
- Hallucination that told a user wrong medical info
- Memory leak that crashed our service
- Vector search returning garbage at scale

Every AI tutorial I'd watched was useless. They showed toy demos that broke the moment real users touched them.

So I rebuilt everything from backend-first principles.

Before I figured this out, I was:

- Copying LangChain tutorials without understanding them
- Treating embeddings like magic
- No idea how to debug AI systems in production
- Terrified of cost explosions
- Building AI features that worked in demos but failed in prod

Sound familiar?

Now I can:

- Design RAG architectures that actually scale
- Build AI agents with proper guardrails and cost controls
- Implement human-in-the-loop systems for high-stakes decisions
- Debug hallucinations systematically
- Explain every architectural decision to a senior engineer

That's the gap. That's what separates "I played with AI" from "I build AI systems."

I'm packaging everything I learned into a 6-week bootcamp:

AI Backend Engineer (Production Systems)

- Week 1: Backend foundations (auth, DB, validation)
- Week 2: Business systems (RBAC, jobs, integrations)
- Week 3: Production hardening (caching, security, observability)
- Week 4: AI infrastructure (vectors, RAG, agents)
- Week 5: AI systems (HITL, memory, monitoring)
- Week 6: Defense (present and defend your architecture)

Every week, you ship production code. Not tutorials. Not notebooks. Real systems.

50 spots. First cohort starts soon.

I'm keeping it small because:

Everyone gets code reviews
Everyone gets feedback
Everyone defends their system

This isn't a course. It's a transformation.

If you've shipped at least one backend service and want to build AI systems.

Join the waitlist: masteringai.dev

The waitlist is the only way in.

1 week ago | [YT] | 0

Solomon Eseme

I've been talking to backend engineers for months about AI.

The same question keeps coming up:

"Should I learn AI? Where do I even start?"

So I wrote the answer I wish someone had given me when I started.

If you're a backend engineer wondering about AI, this is for you: blog.masteringbackend.com/why-backend-engineers-sh…

1 week ago | [YT] | 0

Solomon Eseme

I’ve reviewed 30+ AI courses.

Most of them completely miss the mark for backend engineers.

Here’s what’s actually going wrong: 🧵

The typical AI course:

- "Here's how LLMs work"
- "Here's the OpenAI API"
- "Let's build a chatbot in a notebook"
- "Congratulations, you know AI!"

Then you try to add AI to your production backend.

And everything breaks.

They teach AI in isolation.

- No auth.
- No database.
- No error handling.
- No logging.
- etc

Just `openai. chat.completions.create()` in a vacuum.

Real backends don't work like that.

AI is a feature inside a system and not the system itself.

They skip the hard parts.

What happens when:

- OpenAI is down?
- The response is garbage?
- The user sends 10,000 requests?
- Your monthly bill hits $50K?

Tutorials don't cover this.

Production will teach you the hard way.

They optimize for shiny AI workspace and not how to actually build production systems

They missed teaching systems that handle failures gracefully, track costs, route uncertain outputs to humans, and can be debugged at 2 am.

No accountability.

You simply watch videos and get a certificate. But not building real systems

Later, you still can't:

- Design an AI system?
- Defend your architecture decisions?
- Debug when it breaks?

A certificate that doesn't prove you can build is just a PDF decoration.

They teach AI like it's magic.

- "The model figures it out."
- "Just tweak the prompt."
- "Use temperature 0.7."

This isn't engineering. They are simply showing you how to use AI.

Backend engineers need to control systems, not use them.

The gap in the market:

Courses teach AI concepts

Backend engineers need AI infrastructure

- How to version prompts
- How to implement fallbacks
- How to track costs per request
- How to build a human-in-the-loop
- How to test non-deterministic systems

This is backend work.

What backend engineers actually need:

- Week 1: Not "intro to LLMs" — but production backend foundations
- Week 2: Not "prompt tricks" — but business logic with AI
- Week 3: Not "deploy to Vercel" — but infrastructure that scales
- Week 4-5: RAG, agents, HITL — built properly
- Week 6: Present it. Defend it. Prove you can build.

I'm building this.

6 weeks. Production code every week. Defense at the end.

For backend engineers who want to build AI systems and not just call APIs.

Announcing soon.

Follow + turn on notifications if you want early access.

What's the biggest gap you've hit trying to add AI to your systems?

Drop it below.

I'm building the curriculum around real problems, not tutorial fantasies.

2 weeks ago | [YT] | 0

Solomon Eseme

Most backend engineers adding AI to their systems are building on sand.

They call an API, and when it works, they just ship it like that

Then production happens.

Here are the 6 layers every AI backend actually needs:

Layer 1: The Foundation

Before you touch AI, your backend needs:

- Proper auth (JWT, sessions)
- Validation that doesn't trust LLM outputs
- Structured logging (you'll need it)
- Error handling that doesn't expose prompts

If you skip this.

Your AI feature becomes a security liability.

Layer 2: The Model Control Plane

This is what separates toy demos from production:

- Prompt versioning (rollback when things break)
- Model fallback chains (when OpenAI is down)
- Cost tracking per request
- Rate limiting per user

Your AI is infrastructure. You should definitely treat it like one.

Layer 3: RAG Infrastructure

"Just use embeddings" is the new "just use regex."

Production RAG needs:

- Chunking strategy (not one-size-fits-all)
- Embedding caching (or watch your costs explode)
- Relevance scoring (not just cosine similarity)
- Citation tracking (users will ask, "Where did this come from?")

Layer 4: Agent Guardrails

AI agents are powerful.

But uncontrolled agents are expensive disasters.

Make sure to add Non-negotiable guardrails:

- Iteration limits (agents love infinite loops)
- Cost ceilings per execution
- Timeout enforcement
- Human escalation triggers

An agent without limits is a billing incident waiting to happen.

Layer 5: Human-in-the-Loop

Here's what nobody tells you:

Production AI systems route uncertain outputs to humans.

You need:

- Confidence scoring
- Review queues
- Approval workflows
- Feedback loops that improve the system

"Fully autonomous AI" is a demo.

HITL is the main thing.

Layer 6: Observability

When your AI feature breaks at 2 am, can you debug it?

Production observability means:

- Execution tracing (input = reasoning = output)
- Token usage per request
- Latency by model/operation
- Audit logging (compliance will ask)

If you can't trace it, you can't fix it.

Most AI tutorials teach RAG and end there.

Production demands all the layers:

- Foundation (auth, validation, logging)
- Model Control Plane (versioning, fallbacks, cost)
- RAG Infrastructure (chunking, caching, citations)
- Agent Guardrails (limits, escalation)
- Human-in-the-Loop (confidence, review)
- Observability (tracing, debugging)

I've spent months turning this into a structured curriculum.

6 weeks. Production code every week. Defense at the end.

If you're a backend engineer who wants to build AI systems properly and not just call APIs.

I'm opening spots soon.

DM "AI BACKEND" or watch this space.

Which layer is your biggest gap right now?

Reply with the number (1-6).

I'll share specific resources for the most common answers.

2 weeks ago | [YT] | 0

Solomon Eseme

What’s is the next concept in backend development you will love to learn and who’s the best person to teach you?

I will invite them to the podcast

1 year ago (edited) | [YT] | 0

Solomon Eseme

Hey backend engineers,

I'm just testing this post feature from YouTube.

What type of content do you want to see more?

1 year ago | [YT] | 1