# Why Your LLM Applications Crash in Production (and How to Fix It Under 15 Microseconds)

> Source: <https://dev.to/girisai/why-your-llm-applications-crash-in-production-and-how-to-fix-it-under-15-microseconds-ca9>
> Published: 2026-06-29 18:44:41+00:00

If you're building applications with OpenAI, Gemini, or LangChain agents, you already know the pain: **Large Language Models are unreliable.**

You ask for a JSON response. You set up a strict parser like Pydantic or Marshmallow. But then:

`}`

.`'id'`

) or `True`

instead of standard double quotes and `true`

.And just like that, **your production API crashes.** 💥

Pydantic is fantastic for validation, but **it is designed to fail.** If something is slightly off, it raises a `ValidationError`

and terminates the flow.

To prevent crashes, developers write endless, messy `try/except`

wrappers and heuristic cleanup codes.

That is why I built ** higi**—a self-healing structural middleware layer that sits directly between raw, volatile LLM strings and your strict business logic.

`higi`

Works
With a single decorator, `@shield`

, you define:

When a malformed string enters your function, `higi`

heals it before it reaches your core logic.

``` python
from higi import shield

# 1. Define schema
blueprint = {
    "status_code": int,
    "message": str,
    "is_active": bool
}

# 2. Define safe fallback
fallback = {
    "status_code": 500,
    "message": "Fallback operational state",
    "is_active": False
}

@shield(blueprint=blueprint, fallback=fallback)
def process_data(clean_data):
    # Guaranteed to never receive malformed keys or wrong types!
    print(f"Executing with: {clean_data}")
```

If an LLM returns this truncated string:

`"{'status_code': '200', 'message': 'LLM output got cut off mid-se`

Here is what `higi`

does in microseconds:

`True`

to JSON `true`

.`"`

, and a brace `{`

are left open. It automatically closes them in correct reverse order: `{"status_code": 200, "message": "LLM output got cut off mid-se"}`

.`"200"`

into an integer `200`

.Resilience shouldn't compromise performance. I ran benchmarks using Python's `timeit`

over 50,000 iterations. Here are the results:

`0.56 μs`

per call.`9.26 μs`

per call.`15.14 μs`

To put this in perspective, an LLM call takes `1,000,000 μs`

(1 second). Running `higi`

adds a negligible **0.0015%** latency overhead to your app, but gives you 100% resilience.

Help build the self-healing Python runtime engine!

`pip install higi`

If you find it useful, leave a ⭐ on GitHub! Let's make production crashes a thing of the past.