GenAI MVPs: The Real Work Begins After You Release
Hard-earned lessons from building and shipping fast in the world of AI assistants and chatflows.

Most AI teams are wasting time in all the wrong places.
I’ve done it myself — spent weeks crafting custom infra, only to scrap it when real users showed up (or didn’t). I’ve also watched clients do the same: hiring engineers, building bespoke backends, chasing the illusion of control.
Here’s what I’ve learned — painfully — about what actually matters when you’re building fast-moving GenAI MVPs like chatbots, assistants, or internal tools.
Lesson 1: Speed > Perfection
The biggest mistake I made early on — both with my own product (CalmWays) and while helping clients — was assuming we needed a perfect setup from day one. We spent time building “proper” backends, crafting scalable infra, and trying to anticipate every edge case.
It felt good. It looked smart. It slowed us down.
And although this idea isn’t new in product development — “build fast, iterate later” — in the AI era, it’s more important than ever. The market is moving at 10x speed. If you're not shipping and learning quickly, you’re outpaced before you even launch.
What I’ve learned is this: even for small MVPs, you're better off starting with existing platforms. Use them to test flows, gather feedback, validate value. Only once something starts to click should you think about architecture, scaling, or custom logic.
I’d rather ship a scrappy prompt flow in 2 days and test it with real users than spend 3 weeks polishing a backend that no one asked for.
AI rewards momentum — not infrastructure.
Lesson 2: Don’t Build What You Can Plug In
One of the smartest decisions I’ve seen teams make — and honestly, something I had to learn the hard way — is to stop building everything from scratch.
Platforms like CrewAI and Dify let you spin up workflows, chatbots, and prompt logic without touching a backend. You can literally go from idea to working demo in hours — not days.
And here’s the best part: these tools unlock experimentation for the whole team, not just engineers. Product managers, designers, and even marketers can tweak prompts, test flows, and validate behavior without needing a new deploy or asking someone to “just change that one line.”
What usually ends up happening — and this is a good pattern — is that you decouple the GenAI side from your core services. Let the AI logic live inside one of these platforms. Let PMs tune and test until it feels right. Then connect the whole thing to your classic services with a clean API.
It keeps your dev cycles lean and your product momentum high.
Plug in what you can. Build only what you must.
Lesson 3: Real Usage Changes Everything
Here’s the thing about GenAI: it’s not deterministic.
You can run a perfectly clean prompt and still get a stupid, out-of-context response — or worse, a hallucination. Sometimes you’ll see wild cost spikes. Sometimes the model just makes things up. It’s unpredictable by nature.
And that unpredictability really shows up once real users enter the picture.
That’s why I always recommend adding feedback mechanisms early. Let users rate responses. Add a simple thumbs up/down. Even on the agent level, you can insert blocks to detect when users are frustrated or confused.
You’ll learn fast:
- What responses feel off
- Where context is leaking
- Where the UX is missing guardrails
- And where your token spend is blowing up
If you wait too long to get this insight — or if your system is too rigid to adjust quickly — you’ll fall behind. Fast.
This stage isn’t optional. It’s where the real product work starts.
Lesson 4: Self-Hosting Is a Later Problem
“What if we need to self-host? What about privacy, lock-in, or long-term costs?”
The good news is — most of the best GenAI platforms are open-source.
You can always bring it home later:
But here’s the reality: you probably don’t need to self-host in the early days.
For most teams, the real goal is momentum — shipping something fast, learning from users, and seeing what sticks. Hosted versions let you do that with minimal setup and zero ops.
Now sure, there are edge cases:
- You’re handling sensitive user data (e.g. healthcare, finance, internal IP)
- Your usage hits cost thresholds that break your margins
- Your security team gives you the look
In those cases, it might make sense to go self-hosted earlier. But for everyone else?
Honestly… who wants to maintain another K8s cluster just to tune a prompt?
Start lean. Scale later.
Lesson 5: Most Chatflows Are Repetitive — Use Annotation to Your Advantage
Here’s something I’ve observed across multiple GenAI projects — especially with RAG-based chatflows:
The majority of user queries tend to follow the same patterns.
It’s not a hard rule, but once you're live, you'll quickly notice repetition. That’s where annotation becomes a superpower.
Platforms like Dify support annotation replies, letting you predefine static responses for common queries.
→ Docs: Annotation Replies
This lets you:
- Bypass the LLM entirely for routine questions
- Cut token costs
- Improve response time and reliability
- Enforce accurate, consistent answers
It’s also a great bridge between GenAI and traditional app logic. You keep the LLM focused on the 20% of edge cases — and handle the rest with annotation.
It’s not fancy. It’s just smart.
Lesson 6: GenAI Monitoring Isn’t Optional
Monitoring always matters — but with GenAI products, what you need to track changes a bit.
You’ll likely want visibility into things like:
- Token usage and latency
- Prompt inputs/outputs
- User feedback signals (especially bad ones)
- Weird edge cases or hallucinations
- How often you’re hitting fallback logic or annotated paths
Some of this you can log yourself, but a lot of teams end up needing custom dashboards or prompt-level traces. That's why tools like Langfuse and Dify are super helpful — they save you from duct-taping logs, spreadsheets, and Notion comments together.
My advice? Keep your monitoring close to your GenAI platform.
Don’t spin up a second system unless you have to.
You’ll move faster, catch issues earlier, and spend less time debugging prompt behavior.
Final Thoughts
The GenAI space moves fast — faster than most teams are used to.
That’s why shipping early matters. Iterating quickly matters. And using the right tools matters even more.
Forget the perfect stack. Forget the over-architecture.
Build something real. See what breaks. Then fix fast.
And when you finally go live?
That’s not the end — it’s the start of the real work.