The Architecture of LLM-Powered Applications: How It Differs from Conventional Software Architecture

As I’ve already explored in my previous articles, LLM-powered applications are having a big impact on the way we think about software development. With this rapid acceleration into AI adoption, many companies and teams are discovering that building applications powered by Large Language Models (LLMs) feels nothing like building traditional software. The patterns are different, the risks are different, and so are the architectural decisions.

LLMs introduce new forms of complexity, probabilistic outputs, emergent behaviour, and ongoing data-driven evolution that fundamentally reshape how we think about system design. To create reliable, safe, and scalable AI systems, we need to rethink architecture from first principles.

So in my next two posts, I want to explore how LLM application architectures differ from conventional ones, why these differences matter, and what architectural patterns are emerging as best practice.

Deterministic vs. Probabilistic Systems

Traditional software architecture has always relied on determinism, the idea that systems behave predictably and repeatably. In a deterministic environment:

• The same input always produces the same output, which makes debugging and validation straightforward.

• Bugs are reproducible, allowing engineers to trace the exact failure path.

• Requirements translate into explicit logic paths, enabling clear acceptance criteria, sign-off, and regression testing.

LLM-powered systems break this paradigm entirely. They operate in a probabilistic space, where behavior is shaped by model weights, context, and statistical inference rather than explicit rules.

In this new world:

• The same input can produce many different, but still “reasonable”, outputs, making traditional expectations of consistency unrealistic.

• Small changes in a prompt or context can dramatically shift results, introducing sensitivity that traditional architectures never had to account for.

• Correctness becomes spectrum-based, not binary—outputs range from excellent to acceptable to unsafe, and “right” depends on intent, context, and interpretation.

This fundamental shift places new demands on software architecture. Instead of designing systems that control behavior through deterministic logic, architects must design systems that constrain, guide, and monitor behavior. The role of architecture becomes ensuring safety, stability, and reliability around an inherently variable component.

Architectural consequence

To safely use LLMs in production, teams must implement multiple protective layers around the model to shape its output into something usable and predictable. These layers may include:

• Validation layers to check for format, correctness, and policy compliance

• Filtering and moderation layers to catch harmful or off-topic responses

• Post-processing layers to structure outputs or cross-check facts

• Fallback mechanisms to route to rules engines, human review, or deterministic logic when the model is uncertain

Architecture isn’t just about connecting components anymore; it’s about managing model behavior. And without these guardrails, even a high-quality model can produce unpredictable, unsafe, or inconsistent production behavior.

Data Becomes the Core Architectural Dependency

In my last post, I explored data in the LLM space and its importance, but I will cover it again to highlight how it impacts system architecture. In traditional software systems, data is important, but ultimately, code is what defines the system’s behavior. Business logic is written explicitly, and data simply flows through predefined rules. The architecture focuses on how that code executes: services, APIs, databases, workflows.

LLM systems flip this relationship entirely. In LLM-powered applications, the data is the behavior. What the model outputs is shaped not by deterministic logic but by the data it has seen, the data it retrieves, and the data the user provides. This means architecture must now account for multiple layers of data influence, including:

• Training data: The foundation that shapes model reasoning and patterns

• Fine-tuning data: Domain-specific inputs that teach the model how your organization communicates and solves problems

• Prompt context data: Everything included at inference time—prompts, system messages, constraints, examples

• Retrieval-augmented grounding data: Up-to-date facts and documents pulled from vector stores or databases to ensure accuracy

• User feedback data: Corrections, ratings, and observed behavior that fuel ongoing improvement

This elevates data pipelines, vector stores, lineage tracking systems, and quality controls to first-class architectural components, not optional add-ons. For LLMs, the reliability of the application is directly tied to the reliability of these data inputs.

Architectural consequence

Your data engineering platform becomes part of your AI architecture, not a separate upstream dependency. The quality, freshness, balance, and provenance of your data directly determine:

• The model’s reasoning accuracy

• The safety of responses

• The consistency of behavior

• The level of bias or drift

• The stability of outputs across time and users

  
  

The correctness of an LLM application is no longer guaranteed by how well you wrote the code; it’s determined by how well you curated, governed, and delivered the data that the model relies on. Without a mature data architecture, even the most advanced model becomes unpredictable.

New “AI-Native” Components in the Stack

Given everything we’ve mentioned, it's clear that modern Large Language Model (LLM) applications are not simply “another service” added to your existing architecture. They represent a shift in how systems are designed, operated, and governed. Unlike classical software stacks built around deterministic logic and modular service boundaries, LLM architectures introduce new layers, new responsibilities, and new patterns that reflect the probabilistic and constantly evolving nature of AI systems.

Below is a breakdown of the core architectural components that differentiate LLM-driven systems—and why their existence changes how we design modern applications.

The Model Layer — Intelligence as a Component

In traditional software, “logic” lives inside code. In LLM systems, logic lives inside models: large, pretrained neural networks.

This layer may consist of:

• Hosted LLM APIs: OpenAI, Anthropic, Azure OpenAI (Fastest path to production; abstraction of infrastructure.)

• Self-hosted open-source models: Llama 3, Mistral, Mixtral (Full control but significant cost/ops overhead.)

• Model personalization: Fine-tuning, RLHF, LoRA adapters (Inject domain knowledge without rewriting code.)

Why this matters: Your core business logic is now learned, not programmed. This flips decades of architectural assumptions.

Context Management Layer, Teaching the Model What It Forgot

LLMs don't store your business-specific knowledge by default. This gives rise to an entirely new layer focused on grounding and context delivery:

• Vector databases like Pinecone, Weaviate, Chroma (Store embeddings for semantic retrieval).

• RAG pipelines (Retrieve the right information at the right time.)

• Embedding models (Convert text to vector representations.)

• Context window optimization (Chunking, summarization, re-ranking strategies)

Why this matters: Traditional architecture has “data storage” and “application logic.” LLM architecture has an additional dimension: what the model knows right now, based on the context you feed it.

Safety & Governance Layers, Guardrails for AI Behavior

Conventional software implements guardrails through structured logic and access controls. LLMs require behavioral governance, not just security.

Key components include:

• Moderation pipelines for input/output filtering

• Policy enforcement through rules engines

• Automated red-teaming evaluators

• Human/automated oversight workflows

Why this matters: You’re not only protecting your system—you’re shaping the model’s behavioral boundaries to prevent hallucinations, bias, and unsafe output. This layer did not exist in classical software engineering.

AI Quality & Evaluation Layer — Testing Probabilistic Systems

LLMs break traditional testing assumptions. There is no fixed “correct output”—only acceptable output.

New testing paradigms emerge:

• Prompt testing suites

• Behavioral regression tests

• Evaluation metrics such as BLEU, ROUGE, embedding-based similarity, and factuality scoring

Why this matters: You’re testing behaviors, not deterministic functions. Quality becomes continuous and statistical, not binary.

Architectural Consequence

In classical architecture, a service is a module; you call it, it runs, and it returns data.

In LLM architecture:

• The model depends on the context layer.

• The context layer depends on the embedding pipeline.

• The model’s output must pass through governance layers.

• The output must be evaluated continuously.

• Everything must evolve as models, data, and prompts change.

  
  

This creates an ecosystem of dynamic interactions, where reliability emerges not from code alone, but from the orchestration of many probabilistic components working together.

Architectural Patterns Shift From Pipelines to Loops

For decades, software architecture has been shaped by a simple and predictable principle: information flows in one direction. Traditional systems follow the classic pipeline:

Input → Logic → Output

Deterministic logic processes structured inputs and produces reliable, repeatable outputs. It’s linear, stable, and easy to test.

LLM architectures break this pattern completely.

Rather than executing a fixed sequence of steps, LLM-driven applications operate through iterative cycles - dynamic loops where the model, the prompts, and the surrounding systems continually refine and reinterpret information.

The LLM Flow: Iterative, Not Linear

Modern AI systems follow a flow more like:

Input → Prompting → Model Output → Evaluation → Correction → Output

The difference is subtle but transformative. Between input and output sits a continuous evaluation and improvement process that may repeat multiple times before generating a final result.

This creates new types of loops that never existed in classical software:

RAG Feedback Loops

Retrieval-Augmented Generation (RAG) isn’t a one-and-done lookup. The system can:

• retrieve documents,

• generate an answer,

• detect uncertainty or hallucination risk,

• retrieve additional documents,

• regenerate a better response.

The architecture becomes recursive—knowledge retrieval improves model output, and model output influences further retrieval.

Self-Correction Loops

LLMs can evaluate their own answers using:

• scoring prompts

• reasoning prompts

• "evaluate and rewrite" patterns

Example: The model generates code, evaluates whether it meets the requirements, and rewrites it. Traditional software never reconsiders its own output—LLMs do.

Tool-Calling Loops

In agent-style systems, the model may:

1.     read the input

2.     decide it needs an external tool

3.     call the tool

4.     evaluate the result

5.     call another tool

6.     refine the plan

7.     generate a final answer

This loop continues until the model concludes it has enough information to respond.

Tools may include:

• APIs

• Calculators

• Vector stores

• External reasoning engines

The architecture, therefore, resembles an orchestrator, not a pipeline.

Human Feedback Integration Loops

Where traditional systems escalate to humans only on errors or exceptions, LLM architectures weave humans directly into the learning cycle.

Humans may:

• approve or reject model outputs

• provide reinforcement signals

• correct data retrieval

• refine prompts

• modify policies

The model then regenerates an improved answer incorporating that feedback.

Architectural Consequence

In classical systems, reliability comes from correct logic. In LLM systems, reliability comes from continuous evaluation.

Because LLMs are probabilistic, not deterministic, the architecture must:

• check their outputs,

• cross-validate against grounding knowledge,

• correct mistakes,

• repeat the cycle until a high-quality answer is achieved.

This makes feedback loops the backbone of modern AI architecture.

Rather than building a static flow of data and logic, architects now design systems around:

• iteration

• evaluation

• correction

• improvement

• governance

The quality of an LLM application is no longer defined by the code alone, but by the strength of the loops wrapped around it.

The Importance of Good Architecture never goes away

Architecture plays a critical role in LLM-powered applications because these systems behave fundamentally differently from traditional software. Instead of executing deterministic logic, LLMs generate probabilistic outputs shaped by data, prompts, and continuous model evolution. This means the architecture must provide the guardrails—such as retrieval layers, safety filters, evaluation pipelines, monitoring loops, and versioning controls—that turn an unpredictable model into a reliable, scalable product. Good architecture ensures that LLM behavior is grounded, safe, traceable, and adaptable, enabling organizations to manage risk, maintain quality, and evolve their AI systems as data, context, and models change over time.