Building a Chatbot Taught Me About LLM APIs

bhaleraoneha — Sat, 22 Aug 2026 12:23:24 +0000

Most people's first experience with an LLM API is deceptively simple: send a prompt, get a reply. It feels like magic, and for a single question-answer exchange, it basically is. But the moment you try to build something that holds an actual conversation one where the model remembers what you said three messages ago you run into a problem that isn't obvious until you hit it: LLM APIs are stateless. Every request is a blank slate unless you explicitly hand the model its own memory.

That was the core challenge behind a recent project I built during my internship a chatbot backed by a real LLM API ([OpenAI / Gemini]) with genuine multi-turn conversation support, not just a scripted request-response loop.

*The problem nobody mentions upfront
*
You can't just "turn on" memory. Every conversation turn has to be manually tracked and resent with each new API call, which means the developer, not the model, is responsible for deciding what counts as context. And that decision has real consequences: send too little history and the bot forgets things it should remember; send too much, and you run into token limits and rising costs as the conversation grows.

This is where most simple chatbot tutorials stop short. They show you how to get a reply from an API, but not what happens once a conversation runs long enough that you can't keep resending everything forever.

*Where the actual engineering happens
*
Solving that meant implementing a context management strategy deciding what to keep, what to drop, and eventually exploring smarter approaches like summarising older parts of a conversation instead of just discarding them. It also meant thinking about the bot's identity through a system prompt, handling API failures gracefully instead of letting the UI break, and treating credentials properly by keeping API keys out of source code entirely.

None of this is complicated in isolation. What's interesting is how much of it is invisible until you actually build the thing yourself. Using a chatbot every day gives you zero insight into how much deliberate design goes into something as basic-sounding as 'remembering the conversation.'

*Why this matters beyond one project
*
LLM APIs are increasingly the backbone of real products: support bots, coding assistants, internal tools, and almost all of them face this same underlying problem: statelessness. Understanding how to manage context, memory, and failure gracefully isn't a niche skill anymore; it's close to a prerequisite for building anything serious with these models.

I wrote up the full technical breakdown, including my actual implementation approach, the mistakes I made along the way, and what I'd improve next — in a longer post here:

ai #llm #chatbot #webdev #internship

Read the full article: https://valentiuskryptix.com/building-a-powerful-ai-chatbot-with-an-llm-api/

DEV Community: bhaleraoneha

Building a Chatbot Taught Me About LLM APIs

ai #llm #chatbot #webdev #internship