Building AI Features: The dirty, hands-on truth

We have been helping complex and regulated enterprises build AI solutions, and I was thinking about how we get an AI feature off the ground. It’s not some clean, linear “task” you check off—it’s a messy mix of planning, building, and fixing stuff as you go. I wanted to jot down the lifecycle we’re dealing with so we’re all on the same page and can work together without losing our minds. Here’s the real deal:

Where is the value?

Before we dive into the messy work of building an AI feature, let’s talk about why we’re even doing this. It’s not just tech for tech’s sake—when done right, an AI feature can drive real business value: happier users, faster workflows, or even new revenue streams. Think a chatbot that cuts support tickets by 30% or a tool that saves sales reps hours digging through docs. But here’s the catch—it’s not cheap or quick. The Total Cost of Ownership (TCO) hits hard: upfront dev time, ongoing data maintenance, cloud bills, token economics and the inevitable fixes when it breaks. Knowing that upfront helps us focus on what’s worth it and dodge the “cool but useless” trap. So, with that in mind, here’s the real lifecycle we’re wrestling with to make it pay off

Planning: Figuring out what we’re even Doing

You can’t just dive in and code. First, we’ve got to hash out the basics:

• What’s the brain?: Are we using RAG to pull answers from a pile of data, an Agent to handle tasks, or some mash-up like Agentic RAG or Graph RAFG? Picking this sets the vibe for everything else.

• Rough blueprint: Sketch out how it’ll work—nothing fancy, just enough to not screw ourselves later on scale or speed.

• Memory stuff: Do we need it to remember the last chat (short-term), dig through docs (RAG memory), or track a process (agent state)? Skip this, and we’re rebuilding later.

Real talk: This part’s a slog, but skipping it means chaos. Maybe we throw it in a GitHub issue to keep it straight?

Data: The messy fuel

AI is useless without data, and this is where it gets practical:

• Where’s it coming from?: PDFs, a database, some API—figure out what we’ve got and how to split it up for feeding in.

• Chopping it up: How big are the pieces (chunk size)? Do they overlap a bit so it’s not gibberish? Pick an embedding model (like something from Hugging Face) to make it searchable.

• Chaining it : For extra oomph, we could layer in Graph to map relationships (like who’s connected to what) or lean on RAG to inject context from the data pile—it makes answers sharper but adds more mess to wrangle.

• Getting it in: Batch it or shove it in real-time? Then, decide how we search—mix keywords and vectors, go all-in on vectors, or tag it with metadata.

Heads-up: Data’s never clean. Versioning it might save us when someone updates a doc and breaks everything.

Queries: Making sense of what people ask

Users don’t always ask perfect questions. Here’s the grind:

• Tweaking questions: If someone says “What’s AI?”, do we rewrite it to “What’s artificial intelligence do?” to get better answers?

• Keeping up: For chats that go back and forth, we need to track what’s been said—tie it to that memory plan, or it’s a mess.

Truth bomb: People ask dumb stuff. Test the weird ones early, or we’re toast.

Prompts: Teaching it to talk normal

This is where we make the AI sound less like a robot:

• Set the tone: Tell it “Be helpful” or “Act like a pro”—simple but huge.

• Templates: Mix user questions with data it finds so it doesn’t ramble. Keep it practical.

• Make it useful: Answers should be clear and do something—bonus if it feels personal.

• Guardrails?: Stopping it from saying crazy stuff is nice, but it takes time. Only if the Product folks push for it.

Hack: Start with a basic prompt and tweak it when it sucks—don’t overthink it upfront.

APIs: Hooking it up

Now we make it real for users:

• The basics: ingestion- Shove data in. retrieval- Grab what matters. chat- Let people talk to it.

• Streaming: Use SSE or something custom so answers type out live—users expect that now.

Reality check: It’ll crash if too many people hit it. Slap on some limits or a cache to survive Day One.

Testing & UI: Does it work?

No point building if it flops:

• Try it out: Throw normal questions at it (“How’s this work?”) and weird ones (“Blahblah123!”).

• UI chaos: Make sure the front end doesn’t choke on streamed answers—those generic SSE tags mean the UI has to flex.

• Full run: Fake a user and see what breaks.

Non-negotiable: If the UI can’t keep up with streaming, we’re dead in the water.

After Launch: Keeping it alive

It’s not “done” when it ships:

• Watch it: Check if it’s slow or spitting nonsense—users will tell us quickly.

• Ethics check: If it’s personal, make sure it’s not creepy or biased. It was a quick scan, not a thesis.

• Plan B: If it blanks, give it a “I don’t know, try this” line so it doesn’t look stupid.

Teamwork: Not screwing each other over

This isn’t a solo gig. To not step on toes:

• Split it into tasks on GitHub.

• Argue about it in issues—async’s fine.

• Link the code to what it solves.

I could kick it off with a doc or issue—yell if that works.

The bottom line

Building an AI feature is a grind— understanding value is tricky, planning’s a pain, data’s a mess, and something always breaks. But if we tackle it together, it’s doable and maybe even cool. What do you think?

Any war stories or hacks from your end? Drop ‘em below—I’m here for it