The 4-Layers Architecture of GenAI Applications

More and more companies are integrating GenAI capabilities into their products or even building. This has led to an ever growing number of concepts and architectural patterns. Architecture patterns like Retrieval Augmented Generation (RAG are common in today's architecture as more novel concepts like Agentic Architectures are increasingly being applied. Just recently, the concept of Model Context Protocol, an open protocol that enables seamless integration between LLM applications and external data sources and tools, has seen increasing adoption.

This fast paced development of architecture concepts can feel overwhelming at times. It’s hard to keep up these days. Especially when considering existing applications and applications that are currently already in development or in production. Should you apply the architecture patterns already or wait until they get more traction?

But often, discussion architectures at the level of individual patterns is not required, or even recommended. When we try to understand GenAI systems from their basic principles and building blocks, we realize that these novel architectures are actually implementation concepts for a more general concept of a GenAI application. And our supporting systems and processes need to be built to work with every architecture since architecture patterns are likely to change.

This article outlines a very high level concept for a typical architecture of GenAI applications: The 4-Layers Architecture which consists of the following elements:

• Input Layer
• Model Layer
• Output Layer
• Orchestration Layer

This architecture is on a very high level of abstraction by intention to enable more general discussions about how GenAI applications are designed, developed and operated throughout their lifecycle. It is inspired by the classical Layered Software Architecture: Presentation Layer, Business Layer, Persistence Layer and Database Layer. But the 4-Layers Architecture is specific to GenAI systems and the applications built upon them.

In the following we will outline the 4-Layers Architecture and how it relates to building reliable GenAI applications and how each layer affects quality, reliability and security of your applications.

The Typical Anatomy of a GenAI Application

The anatomy of GenAI applications differs significantly from traditional digital applications. Understanding these differences is key to understanding the challenges for developing such applications. Taking several steps back to widen our perspective, we see that GenAI applications typically consist of four separate layers.

The 4 Layers of GenAI Applications

Input Layer: The input comprises all the data that is sent to the GenAI model to generate an output. This includes the input from the users, but also additional, potentially large data context that is needed to generate an output. The quality of the data is often varying, depending on the systems where it is queried from. Retrieval Augmented Generation is one key architectural pattern at this level, with various implementation strategies.

Model Layer: The model layer represents the model your application is using with the additional parameters and internal history. Applications often even use multiple models that are specialized with model parameters or model fine-tuning. A critical element is the Prompt Template that contains the core instructions for the model and aligns the data context with the intended use case. The prompt template, combined with the data context, represent the prompt that is sent to the model.

Orchestration Layer: This layer handles the flow of information between system components, manages API calls, implements retry mechanisms, and controls the application's overall logic. Increasingly, Agentic Architecture patterns are used to build complex use cases. For complex use cases, the orchestration layer represents critical portions of the business logic (which tool or service to query in a given scenario), and therefore needs to be included in the quality management and operations strategy.

Output Layer: This final layer processes the model's response to meet the expected output. This includes processing structure output to transform it into the required format, or other forms of post-processing to suit downstream systems and end-users. An important, often overlooked capability of the output layer is to evaluate the results to filter out inappropriate content and low quality responses. LLM model providers (like OpenAI or Anthropic) invest substantial efforts in making their models safe and secure with techniques like Reinforcement Learning with Human Feedback (RLHF), which basically requires humans to rate and adjust the outputs of models. But business applications need an additional level of validation to make sure that the business context is respected and the output is safe in the given context. Concepts like online evaluations (often referred to as guards or guardrails) are a core part of the output layer to implement this filtering and safeguarding logic.

This was a short introduction to the 4 layers and the capabilities they represent in typical GenAI architectures. Let’s see how this enables better understanding of applications and better conversations about how to test and operate these systems.

Why the 4-Layers Architecture Help Building better Systems

Understanding this basic anatomy of GenAI architectures reveals why GenAI applications require specialized development, quality management, and operations approaches. Each layer introduces distinct complexities that traditional methods cannot adequately address, creating unique challenges for development teams striving to build reliable AI-powered systems.

The 4-Layers Architecture enforces a strict, one-way dataflow with clear responsibilities of the individual layers that makes it possible to observe and analyse data flowing through the architecture and provides natural hook points for monitoring and checks. This flow enables effective monitoring and testing for the application as a whole as well as for each of the individual layers.

The actual concepts used to implement the inner workings of each layer therefore can be considered implementation details. These details can change over time, while teams gain more experience with GenAI in general and with the growing number of technologies and technical concepts. Changing implementation details like the type of retrieval strategy for RAG, can be done throughout the applications lifecycle without invalidating the general development, testing and operational strategies. This ensures that investments in tooling and processes are long-term and not changing with individual implementation strategies.

With the basic understanding of the 4-Layer Architecture we can now better understand the development and maintenance challenges. So, let’s turn to the topic of ensuring that applications generate reliable, high-quality results.

The Impact on Quality and Reliability

The 4-Layers Architecture enables us to define reliability (which includes perspectives like quality, correctness, safety and security) at the level of the application or on the level of the individual layers. Both levels of abstractions are important to consider to ensure that the quality management strategy effectively catches any unwanted results.

On the Application Level, we can define criteria for quality and reliability for the final output generated by the applications. This is a good starting point for a testing or evaluation strategy but fails to provide detailed information about potential root cases. This can lead to significant efforts for resolving detected problems. A common strategy is to use reference datasets (also known as Golden Records) to compare generated results against validated references.

The application level is basically a blackbox evaluation of the systems behaviour, catching significant divisions in the output. But this level of sensitivity and abstraction leaves development teams with insufficient information to dig deeper if a problem is detected. An effective approach to overcome this limitation is to define specific criteria for the individual layers of the architecture. Testing at the Layer Level enables teams to identify root causes much faster, leading to greatly improved turn around times for fixes.

The following lists represent typical evaluation strategies for the individual layer to illustrate the concept of mapping the quality management strategy. Mapping criteria to the individual layers avoids blind spots in terms of quality and reliability of the generated result at a very detailed level.

Evaluation Strategies for Input Layer:

Strategy	Description
Contextual Relevance	Analyzes if the provided context is relevant to the user query.
Data Quality	Measures the quality and reliability of data sources being used to augment the model input.

Evaluation Strategies for Model Layer:

Strategy	Description
Content Safety	Evaluates outputs for harmful, biased, or inappropriate content before delivery to users.
Prompt Effectiveness & Alignment	Evaluates whether the constructed prompts provide clear instructions and sufficient guidance for the model, and if the model respects the instructions.

Evaluation Strategies for Orchestration Layer:

Strategy	Description
Tool Calling	Checks if the expected tools or agents are called for the given task.
Latency Measurement	Evaluates the system's ability to maintain response times within acceptable thresholds.

Evaluation Strategies for Output Layer:

Strategy	Description
Answer Relevance	Determine if the response directly addresses the user's query.
Response Format Compliance	Ensures that outputs adhere to expected structure and formatting requirements.
Spelling and Entity Match	Checks if the output contains general spelling and grammatical errors and if specific entities like persons, location and events are correctly spelled.
Guardrail Detection	Checks if specific inputs and data conditions trigger the expected guardrails to safeguard answers delivered by the system.

This list is intended to give you a better understanding of how evaluation criteria can be mapped to the individual levels. There are many more evaluation criteria available and the list is far from being exhaustive. Which evaluation criteria depends on your specific use case and its specific quality and reliability requirements.

The Missing Link in GenAI Development: Quality at Every Layer

The 4-Layers Architecture is not meant to be used for designing the systems in detail. More specific architectures from traditional software development or GenAI-specific architectures (RAG, Agents, MCP) are much better suited. The power from the 4-Layers Architecture comes from its level of abstraction that moves above technical details of the actual implementation to a general understanding of the core capabilities of such systems. This enables cross-functional teams of technical and domain experts to discuss important topics like testing, evaluations, and observability at a more appropriate level.

For quality management in particular, the 4-Layers Architecture helps teams to define specific criteria for the individual layers making it easier to detect issues and find root causes. This is especially important for complex, opaque systems like GenAI applications with their complex models.

How ZENTICS Helps Teams to Ship Better GenAI-Applications

While organizations race to adopt generative AI capabilities, a critical gap has emerged between proof-of-concept demos and production-ready applications. At Zenetics, we've observed this pattern repeatedly across industries: impressive AI prototypes that falter when faced with real-world demands for reliability, consistency, and safety.

This is precisely why we developed the 4-Layers Architecture framework.

As pioneers in comprehensive LLM application quality management, ZENETICS has worked with many top-engineering teams struggling to transition their GenAI projects from exciting demos to dependable production systems. Our experience has shown that traditional quality assurance approaches fall short when applied to the unique challenges of generative AI applications.

The 4-Layers Architecture isn't just a theoretical model—it's the foundation of our entire product suite. ZENETICS enables you to define, measure and improve the quality of your applications at every layer of the architecture. Leading to improved user experience and increased development velocity.