Beyond the Demo: Real-World Problems with LLM API Implementation

Why Put an LLM API Call in Your Code?

One powerful way to think about using an LLM API in your code is this: you’re outsourcing a piece of logic, sometimes simple, sometimes deeply complex.Imagine you’re building a product and there’s a chunk of logic that, in an ideal world, with enough time, data, and compute, you’d just build yourself. Maybe it’s as straightforward as mapping user intent,or maybe it’s something far more involved, like image classification, summarising customer messages, or transcribing and interpreting voice.What LLMs let you do is delegate that chunk of functionality. You send in some text, a prompt, maybe some metadata or even multimodal input, and the API returns an intelligent response, this can be a decision, a summary, a label, a suggestion. And while it’s powered by a massive neural network under the hood, it’s often helpful to think of it as a virtual code block that speaks natural language.You’re not replacing all of your logic with magic. Instead, you’re strategically offloading tasks that would be too time-consuming or expensive to hardcode yourself.

That is precisely what makes LLM APIs a huge leverage point, especially for solo devs or small teams. Suddenly, you’re shipping features that used to require a dedicated NLP engineer or a whole software engineering team!

That’s the key trade-off here: instead of building, training, and maintaining custom logic, you call out to a hosted intelligence, and let it do part of the work. It’s a bit like hiring a powerful but fuzzy-thinking assistant: fast, capable, and scalable, though not always predictable.

And this trade-off is especially powerful for MVPs and startups. Want to build a prototype that classifies, recommends, parses, or chats? Plug in an LLM, wrap a UI around it, and suddenly you’re shipping product. You’re not cutting corners, you’re accelerating what would otherwise take weeks or months.

Whether it’s for agent logic, data labeling, semantic search, or user interface enhancements, making the decision to integrate an LLM isn’t about chasing hype. It’s about choosing the right abstraction layer for your current constraints and ambitions.

What Changes When You Integrate an LLM Call?

So you’ve made the decision: you’re going to include an LLM API call in your code. It makes sense, you’ve weighed the trade-off: rather than building out a massive chunk of logic yourself, you’re outsourcing it to a model that can do it better, faster, or just more affordably at your current scale.

But it is precisely this decision that changes the game. You’re no longer writing purely deterministic code, you’re writing code that talks to intelligence. That comes with new constraints, new costs, and a few big mindset shifts.

Those are the three biggest changes that are important to be aware of:

1. Cost Is Now a Core Part of Your Architecture

With traditional code, execution cost was mostly invisible: CPU cycles, RAM usage, maybe some bandwidth. But with LLMs, every call translates into real monetary costs, and those costs may quickly scale with input/output size and model quality.

Here’s a rough breakdown:

• 🪶 Lightweight models: AWS’s Titan Text Lite or Mistral-based models can cost around $0.03 per million input tokens.

• ⚡ Powerful models: Models like Claude 3.5 Sonnet or GPT-4 Turbo can range from $0.50 to $3+ per million input tokens.

• ⚠️ Token math: A typical prompt-response pair can consume 10K–15K tokens total, especially if you’re working with long context (e.g., documents, emails, chat history).

So your system architecture now has to factor in financial cost per request, and that varies depending on:

• Which model you use (quality vs. price)

• How verbose your prompts and responses are,

• How frequently you call the model.

It’s a new kind of performance tuning: balancing quality vs. cost. In some cases, a more affordable model is good enough, especially when it comes to keyword extraction, classification, or light summarization. Other times, the accuracy gain of a bigger model is worth the investment.

2. The Output Is Non-Deterministic

This is one of the most fundamental changes to how your code behaves. Traditional functions always produce the same output for a given input. But with LLMs? Same prompt, different day — possibly different answer.

That’s not necessarily a flaw, it’s how creativity, generalization, and nuance work. On the other hand, It means you need to design your system defensively:

• If you’re building a summarizer or a chatbot, variation is fine, even expected.

• If you’re building decision-making logic or agents that act based on LLM output, you need guardrails.

Even with a clear prompt, there’s always a slight chance, 5%, 2%, maybe less, that the model says or decides something incorrect or even irrelevant.

We’ll cover how to deal with this (e.g., retries, validations, constraint prompting) in the next section.

3. Response Time Becomes a Bottleneck

Last but not least: LLMs aren’t instant. You’re making a network call to a large model that may be distributed across multiple GPUs in a remote data center. That adds latency.

Depending on the model and complexity of your prompt:

• Small models might respond in under 1 second,

• Mid-range models take 2–6 seconds,

• Larger models (or longer outputs) can take 10–30 seconds, or more.

This affects how you structure user flows: Do you show a loading spinner? Do you stream responses? Do you need fallback behaviour if the model takes too long? Should you pre-cache predictions or use async background calls?

Suddenly, your backend isn’t just running logic; it’s waiting on someone else’s very smart, very slow intern.

Deep Dive: Solving for Response Time and Non-Determinism

Once you start building with LLMs in production, the magic wears off quickly; not because the models are any less impressive, but because the engineering realities start to kick in.

Therefore, we will take a deep dive into two of the biggest practical challenges of LLM-integrated engineering: response time and non-deterministic output.

1. Tackling Response Time

As the co-founder of Ewake.ai, I’ve been deeply involved in building AI agents powered by large language models. One of the most persistent and complex challenges we’ve faced is managing response time, not just to deliver a smooth user experience, but also to ensure efficient system architecture and cost control.

Based on what we’ve learned through this journey, here are the key strategies that have proven most effective:

A. Iterate Across Models and Regions

If your system doesn’t rely on a specific model (like GPT-4 or Claude 3), you can implement model rotation strategies. This means calling different models based on:

• Current latency benchmarks,

• API rate limits,

• Regional availability.

This not only improves average latency but also avoids hitting the rate limits of a single provider or endpoint. Some LLM providers even offer multi-model endpoints or routing logic you can build on top of.

B. Keep Prompts Short. Seriously.

This one seems obvious. But what’s less obvious is that prompt length increases latency non-linearly. Doubling your prompt length won’t just double your latency, it could triple or quadruple it depending on how much context the model has to parse.

Plus, longer prompts risk triggering known LLM weaknesses like the “lost in the middle” phenomenon, where the model misses important information buried deep in the context.

Instead of one massive prompt with long chain-of-thought reasoning, break your tasks down into smaller, simpler calls. That way:

• Each call is faster,

• The model is less likely to get confused,

• You get more reliable, structured behaviour.

This kind of “task decomposition” not only improves latency but often improves output quality as well.

C. Avoid Confusing Prompts

Here’s a less-discussed but surprisingly common issue: prompt confusion slows the model down.

At Ewake.ai, we ran into cases where the model would stall or timeout, and the root cause wasn’t model size, it was contradictory instructions in the prompt.

Example:

• You say: “Output a JSON with fields A and B.”

• Later in the same prompt, you give an example with fields A and C.

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Now the model’s stuck trying to resolve an internal conflict. It might retry internally, hesitate, or hallucinate an inconsistent output, all of which waste time.

To avoid this:

• Be precise and consistent with prompt structure.

• Ensure there’s no mismatch between what you’re asking for and the examples you provide.

• Avoid unnecessary complexity, clarity beats cleverness.

  
  

2. Dealing with Non-Deterministic Outputs

Another fundamental aspect of working with LLMs is their inherent non-determinism. They don’t always produce the same output, even when given identical input. This variability is part of what makes these models so powerful, enab rich, dynamic responses. But it also introduces a layer of unpredictability, turning the system into something of a fuzzy black box.

Through our work, we’ve developed several effective strategies for embracing that fuzziness, without letting it compromise the integrity or reliability of our systems.

A. Use Guardrails Inside the Prompt

Before you even hit the API, the first line of defense is your prompt design.

This means:

• Giving clear instructions for what you expect (formatting, style, structure),

• Being explicit about edge cases,

• Using examples that reinforce the behaviour you want.

If you’re generating structured output, whether it’s JSON, XML, or tool call, even minor phrasing errors in your prompt can cause unexpected drift. We’ve found that small prompt adjustments can make a surprisingly big difference in stability and accuracy:

• “Respond only with a JSON object containing fields A, B, and C.”

• “Do not add any explanation or commentary.”

• “Field C must always be a lowercase string, even if not found.”

The clearer your prompt, the more consistent your output.

B. Don’t Be Cruel. Add a Post-Processing Layer

Even the best prompts can’t guarantee 100% perfect responses, because that’s not what these models are optimized for. So if you’re treating the LLM as a deterministic service, you’ll inevitably run into frustrating edge cases.

Instead, LLM can be considered as getting you 80% down the road, then let a thin post-processing layer handle the rest.

At Ewake, whenever we use an LLM to generate structured data, we consistently follow this approach:

• Output validation (e.g., does it match schema?),

• Logical consistency checks (e.g., do related fields align?),

• Optional fallbacks or retries if the output doesn’t pass.

This mindset, giving the LLM room to be imprecise, but building systems that handle it gracefully, leads to far more robust results.

C. Think of LLMs as Temporary Scaffolding

Finally, here’s a strategic mindset shift: your LLM API calls of today’s code, don’t have to be there tomorrow!

Back in section one, we talked about using LLMs as a trade-off. a shortcut when you don’t have time, budget, or team bandwidth to implement something yourself.

But as your product evolves, you might replace some of those API calls:

• You start with one-shot prompting,

• Then evolve to multi-step agent flows,

• Eventually, you replace some of those calls with hardcoded logic, retrieval systems, or classic machine learning as your domain becomes clearer.

That’s a good thing. It’s not about abandoning LLMs, it’s about using them wisely: where they help you go faster, and moving away from them when you gain confidence in other solutions.

Wrap-Up: LLMs as Engineering Trade-Offs

LLMs bring enormous power, the ability to drop in advanced language understanding, decision-making, and even reasoning with just an API call. But they also shift how we think as engineers:

• From deterministic to probabilistic systems,

• From code-only logic to mixed human–AI flows,

• From building everything ourselves to strategically outsourcing cognition.

The key is staying aware of the trade-offs:

• You’ll pay in cost, latency, and uncertainty,

• But you gain in speed, versatility, and flexibility.

Over time, you’ll develop a sense for the right balance, when to rely on the LLM’s flexibility, and when to replace it with something more deterministic and reliable. Striking that balance is part of the art of modern software design.