# The LLM Is Not Your Application: A Systems Approach to Generative AI
We are living through a Cambrian explosion in AI. The capabilities of large language models (LLMs) have captured the public imagination, and for good reason. Demos that generate code, summarize complex documents, or create stunning prose feel like magic. Yet, many organizations moving from these impressive demos to production-ready applications are hitting a wall. Their AI-powered features feel brittle, unpredictable, and difficult to integrate into existing, deterministic workflows.
The problem isn’t the model; it’s the mindset. We have been treating the LLM as the entire application, when we should be treating it as a powerful but fundamentally new type of component: a probabilistic, non-deterministic processor. The future of applied AI doesn’t lie in simply finding the “perfect prompt.” It lies in wrapping these powerful models in the robust scaffolding of traditional software engineering—a practice I call AI Systems Engineering.
### From Probabilistic Guesses to Reliable Outcomes
At its core, an LLM is a massively complex statistical engine. When you give it a prompt, it doesn’t “know” the answer; it calculates the most probable sequence of tokens to follow. This is a fundamental departure from the deterministic logic that underpins virtually all other software. An `if` statement is always an `if` statement. A database query returns predictable results based on its inputs. An LLM’s output, by contrast, can vary even with the same prompt.
Building a reliable application on top of this probabilistic core requires us to stop trusting the LLM’s output implicitly. Instead, we must architect systems that constrain, validate, and orchestrate the model’s behavior. The goal is to channel its immense generative power into a narrow, predictable, and useful channel.
This systems-level approach generally involves three key layers:
#### 1. Structured Inputs and Outputs
The first step is to move away from free-form text as the primary interface. Raw, unstructured paragraphs are difficult for downstream systems to parse and act upon. The solution is to enforce a schema.
Modern LLM APIs are increasingly built for this. Features like OpenAI’s “function calling” or Google’s “tool use” are not just neat tricks; they are essential architectural patterns. They allow you to define a strict JSON schema that the model must adhere to in its response. By prompting the LLM to populate a predefined structure, you transform its output from a creative suggestion into a machine-readable payload. This is the critical bridge between the probabilistic world of the LLM and the deterministic logic of your application code.
#### 2. Validation and Guardrails
Even with a perfect schema, the *content* of the LLM’s output can be incorrect. It can hallucinate facts, generate invalid data (e.g., a non-existent product ID), or produce unsafe content. A production-grade AI system cannot afford to pass this output directly to the user or another service.
This is where a validation layer becomes non-negotiable. This layer is pure, traditional code that acts as a gatekeeper.
* **Schema Validation:** Does the output strictly conform to the expected JSON schema?
* **Fact Checking:** Can key entities or claims be verified against a database or a trusted external API?
* **Business Logic:** Does the generated output comply with established business rules? (e.g., Is the proposed discount within an acceptable range?)
* **Safety Checks:** Does the output contain harmful, biased, or inappropriate content?
This layer treats the LLM’s output as untrusted input, just as we would with any user-submitted form.
#### 3. Feedback and Correction Loops
The most sophisticated systems go one step further: they create automated feedback loops. When the validation layer detects an error, the system doesn’t just fail; it attempts to self-correct.
A common pattern is a “critic” or “re-phrasing” loop. The system takes the invalid output from the first LLM call, appends an error message explaining *why* it was invalid (“Error: The `customer_id` provided does not exist in our database.”), and feeds this entire context back into the LLM for a second attempt. This iterative process of generation, validation, and correction dramatically increases the reliability of the final output. It mimics the human process of drafting and revising, but at machine speed.
### Conclusion: Engineering the Magic
The magic of generative AI is real, but to harness it for real-world work, we need to be more than just prompters—we need to be engineers. The LLM is a revolutionary component, but it is still just one component in a larger system.
By embracing a systems-thinking approach—enforcing structure, validating outputs, and building corrective feedback loops—we can transform brittle demos into resilient, production-grade applications. The future of AI isn’t just a bigger model; it’s a better-architected system built around it. It’s time to put on our engineering hats and build the machinery that will truly put this magic to work.
This post is based on the original article at https://techcrunch.com/2025/09/23/tim-chen-has-quietly-become-of-one-the-most-sought-after-solo-investors/.
