But the next day, the same prompt gives you something else: “Hi! What can I help you with?” And the day after that: “Hey there! How may I assist you today?”
Imagine this: You’re sitting at your desk, staring at a brand-new API key from OpenAI or Google gemini or Anthropic, thinking you’ve just unlocked the secret to adding next-gen intelligence to your app. The idea is simple — just send a text prompt to the model and get a response. It feels almost magical. You’re convinced that your product is about to get a massive upgrade.
But as you hit “Run” for the first time, your excitement meets reality. The response isn’t quite what you expected. It’s not even close. You tweak the prompt, try again — still odd. Suddenly, the fantasy of seamless integration turns into a frustrating loop of trial and error.
Welcome to the world of building LLM-based applications. It’s not that the models aren’t powerful — they absolutely are. The challenge is in understanding how unpredictable they can be and why that makes building real-world applications an entirely different game.
The Illusion of the Simple API Call
In theory, using an LLM feels like making a simple API call. You pass in a text prompt and get back a response. Simple, right? Not really.
Imagine trying to build a customer support chatbot. You carefully craft a prompt that says, “How can I help you today?” You hit the API, and the response comes back as, “Hello! How can I be of assistance on this fine day?” That’s perfect — exactly what you wanted.
But the next day, the same prompt gives you something else: “Hi! What can I help you with?” And the day after that: “Hey there! How may I assist you today?”
Suddenly, your tests start failing because your app was built to expect one consistent output, not a rotating cast of responses. It’s like hiring an assistant who uses a different greeting every time they answer the phone — charming in theory, but chaotic when you need consistency.
You might be thinking, “Why doesn’t it just say the same thing every time?” The problem is that LLMs aren’t deterministic. They’re probabilistic models designed to predict the most likely next word or phrase. Every time you call the API, it might choose a slightly different path. It’s not a bug; it’s just how they’re built.
Building Logic on Unpredictable Foundations
Imagine trying to teach someone to always say “Yes” when asked for permission. You train them diligently, but sometimes they say “Sure,” or “Of course,” or even “Why not?” That’s an LLM in a nutshell. It doesn’t think like a human — it doesn’t “decide” to use synonyms, it just calculates probabilities and generates what feels natural.
Now imagine writing automated tests for this. You might expect the output to be “Yes,” but it could come back as “Definitely” or “Absolutely.” Your test fails, even though the intent is the same. You can’t just write an assert.equal() and call it a day.
It’s not just frustrating; it’s fundamentally challenging. Traditional software is based on predictable input-output relationships. LLMs break that rule. You’re not building a fixed function. You’re building around a system that has an inherent degree of randomness, and that means adapting your entire way of thinking about testing and validation.
When Prompts Aren’t Code
At this point, you’re probably thinking, “Okay, so I’ll just make the prompt super clear and precise. Problem solved!” But here’s another reality check — prompts aren’t functions. They’re not precise, and they don’t always interpret things the way you think they will.
Picture this: You’re writing a prompt for a summarization tool. You say, “Summarize the following text in two sentences.” It works beautifully. You’re thrilled. But then you try, “Give me a concise summary of this text.” Suddenly, you get three sentences or a verbose paragraph.
Changing just a few words makes a surprising difference. It’s not that the model is being stubborn — it’s interpreting your prompt differently because it’s essentially pattern matching, not following strict rules.
To make matters worse, prompts that work perfectly today might break tomorrow, especially if the model version changes or you decide to alter the context slightly. Your careful tuning suddenly falls apart, and you’re back to square one, experimenting with prompt wording like some kind of arcane ritual.
Memory? Not Quite
Now you’re probably thinking, “If I can just get it to remember context, it’ll be fine.” That’s another trap. LLMs don’t inherently have memory. They process inputs and generate outputs without retaining what happened previously — unless you explicitly include past interactions in your prompt.
Imagine you’re building a virtual assistant that remembers what the user said earlier. You try including the entire chat history in the prompt every time, but it quickly becomes too long to fit within the model’s token limit (but now models have bigger context length like 128k or 200k but would still be exhausted from continuing chatting for a long time). You have to get creative maybe storing past conversations in a database and feeding back only the most relevant parts (you could see now these models uses something called as memory and often says “memory updated”).
Debugging the Invisible
Just when you think you’re getting a handle on things, the LLM suddenly starts spitting out nonsense — verbose responses where it once was concise, or contradicting itself midway through a paragraph. You didn’t change anything significant, so what gives?
Debugging an LLM feels like chasing shadows. There’s no stack trace, no line numbers pointing you to the issue. All you have are logs of the input and output, with no insight into what went wrong. It’s as if you’re trying to fix a car without being allowed to open the hood.
You might tweak the prompt, rephrase your input, or even downgrade to a simpler model, but every change is a shot in the dark. There’s no clear, rational way to debug like you would with traditional code.
Scaling: The Reality Check
So you finally get your app working in a controlled environment. It’s passing your tests — at least, most of the time. You’re ready to scale up and serve real users.
That’s when performance issues start to bite. LLMs are heavy and slow compared to regular APIs. Calling them for every user query is like running a marathon every time someone asks a simple question. You have to cache responses, chunk long texts, and deal with timeouts.
Oh, and every call costs money. A lot of it. Suddenly your app’s delightful conversational abilities are burning a hole in your budget. It’s not just about making it work, it’s about making it work efficiently at scale.
Right now some companies like Cerebras and Groq have managed the latency time but still on production the cost is too high.
Taming the Chaos
So, what’s the way forward? The secret is to stop fighting the unpredictability and start designing around it.
Layered Orchestration: Treat the LLM as just one part of a pipeline. Use retrieval systems to find relevant context and only call the LLM when necessary. Modular Prompts: Break prompts into manageable parts and keep them consistent. Document what works and why. Guardrails and Fallbacks: Implement backup logic for when the LLM’s response is clearly wrong or too slow. User Expectations: Educate users that the AI can make mistakes and occasionally drift off-topic. Embracing the Uncertainty Building with LLMs is like creating art on shifting sand. Just when you think you’ve perfected your approach, something changes a model update, a tweak to the prompt and the whole thing wobbles.
But that’s also the beauty of it. Instead of seeing it as purely frustrating, see it as a challenge to build systems that embrace uncertainty. The most successful LLM applications are the ones that anticipate chaos and build flexible structures to handle it.
It’s not easy. But if you lean into the unpredictability and learn to design around it, you’ll unlock powerful, dynamic capabilities that deterministic software just can’t match. And that’s where the real magic lies.
Let’s join hands and empower the AI community:
Looking for secured AI services or consultancies? Checkout our AI services at: https://synbrains.ai, https://www.linkedin.com/company/synbrains/
Or connect with me directly at: https://www.linkedin.com/in/anudev-manju-satheesh-218b71175/
Buy me a coffee: https://buymeacoffee.com/anudevmanjusatheesh
LinkedIn community: https://www.linkedin.com/groups/14424396/
WhatsApp community: https://chat.whatsapp.com/ESwoYmD9GmF2eKqEpoWmzG
