The Testing Impact of Architecture in LLM-Powered Applications

In my previous post, we explored the big changes required in architecture to make LLMs successful. As those who are familiar with me will know that quality and testing are vital aspects of software architecture to me. So, I feel like I wouldn’t be able to explore the topic of software architecture without aspects of testing and quality.

Testing and QA Must Be Built Into Architecture

In classical software engineering – at least at a fundamental code level - testing often tends to follow a more logical path:

You validate functions, rules, and logic paths - with potentially complex data scenarios at times. If the logic is correct, the system is correct. Bugs are often reproducible. Inputs produce predictable outputs. Testing sits at the end of the pipeline.

LLM requires a different type of thinking around this.

Large Language Models don’t follow deterministic rules; they exhibit behavior shaped by data, prompts, context windows, and probabilistic reasoning. As a result, quality can no longer be validated by checking logic branches or unit tests alone.

Testing Is About Behavior, Not Code

Because LLM outputs can vary, you test how the system behaves, not whether a specific logical path is executed. This requires new testing patterns that traditional architectures were never designed to support:

• Golden Datasets: Reference questions and expected outputs used to benchmark the model and detect regressions.

• Scenario-Based Testing: Evaluating complex, real-world tasks (e.g., "As a customer, I want to…") to validate reasoning, not just correctness.

• Prompt Regression Suites: Systematically testing prompts to ensure updates, model swaps, or RAG changes don’t subtly break behavior.

• Bias & Fairness Testing: Assessing whether the model treats different demographic groups equitably, a category of testing historically outside the scope of software QA.

• Adversarial Attack Testing: Validating resilience against jailbreaks, prompt injections, and manipulated inputs.

• Drift Detection: Monitoring model behavior over time to detect when outputs start shifting unintentionally.

A lot of this is common in behavioural or exploratory testing techniques, but not at a unit or component level that is required for CI/CD pipelines. LLMs force architecture to treat testing as a dynamic, ongoing evaluation discipline.

Architectural Consequence

In LLM systems, testing is no longer a “downstream activity” performed after development. It becomes a core architectural capability.

This means:

• Testing infrastructure must be built into the architecture itself.

• Evaluation pipelines run continuously, not just at release time.

• Behavioral tests live alongside code, prompts, and data pipelines.

• AI quality dashboards become part of the production environment.

• Model monitoring is as important as application monitoring.

Instead of “test after you build,” the mentality becomes:

“Continuously evaluate as the system behaves.”

Modern LLM systems succeed not because the code is perfect, but because the architecture can detect, measure, and correct imperfect behavior at scale.

LLM Observability Requires Behavioral Signals

In traditional software systems, observability revolves around infrastructure and application health. You monitor latency, error rates, throughput, and saturation; signals that indicate whether the system is running efficiently and reliably. When performance metrics look good, you typically assume the system is healthy.

LLM applications again change this assumption.

A model can be fast, stable, and efficient—and still produce harmful, incorrect, biased, or nonsensical outputs. Traditional observability doesn’t capture this. With LLM-powered systems, the biggest risks aren’t operational failures—they’re behavioral failures.

To ensure reliability, observability must expand beyond performance and include continuous monitoring of how the model behaves:

• Toxicity Events: Detecting harmful, offensive, or unsafe responses in real time.

• Hallucination Rates: Measuring how often the model confidently produces factually incorrect outputs—one of the most critical production risks.

• Prompt Failures: Identifying situations where the model misunderstands instructions or produces unstable responses to the same prompt.

• Drift: Tracking changes in model behavior over time, especially after fine-tuning, RAG updates, or system retraining.

• Conversation Quality Metrics: Monitoring coherence, helpfulness, relevance, and task completion across multi-turn interactions.

• User Sentiment and Feedback Loops: Capturing satisfaction, corrections, and frustration signals directly from user interactions, valuable behavioral indicators.

These signals help answer the essential question: Is the model behaving the way we expect it to?

Architectural Consequence

For LLM applications, observability evolves from measuring system health to measuring model behavior health.

This creates new architectural implications:

• You need pipelines that score and log model outputs, not just infrastructure events.

• Monitoring dashboards must include behavior-specific KPIs (e.g., hallucination rate per 1,000 queries).

• Alerts must trigger on unsafe or low-quality outputs, not just 500 errors.

• Output evaluation services sit alongside logging and metrics collection.

• Human feedback and moderation signals feed into continuous monitoring loops.

• Behavioral observability becomes essential for compliance, ethics, and model trust.

In short, a system can perform well while the model performs poorly. Architecture must be capable of detecting both the model and the system independently.

LLM Architectures Must Handle Human-in-the-Loop by Design

LLMs introduce a level of unpredictability that traditional software systems simply don’t have. Because they operate probabilistically, and can hallucinate, omit context, or misinterpret intent, there are scenarios where fully autonomous operation isn’t safe or appropriate. This is especially true in regulated environments, high-risk decision-making, or customer-facing workflows where accuracy and compliance matter.

To mitigate these risks, architectures must embed structured human oversight. This isn’t an optional add-on; it’s part of ensuring responsible, reliable behavior at scale.

Human-in-the-Loop Is Not Optional

Certain workflows require humans to review, validate, or correct outputs to maintain quality and trust. These typically include:

• Approval Steps: Critical decisions - like financial recommendations, legal interpretations, or policy exceptions - must have a human checkpoint before proceeding.

• Escalation Paths: When the model produces uncertain, risky, or low-confidence responses, the system should automatically route the case to a human reviewer or specialist.

• Correction Workflows: Reviewer edits should be captured, versioned, and fed back into evaluation pipelines or fine-tuning loops to steadily improve model reliability.

• Crowd or Expert Evaluation: Larger systems may rely on distributed reviewers or subject-matter experts to validate outputs at scale, especially for training and continuous quality scoring.

This oversight layer prevents the AI from drifting into unsafe or incorrect behavior while also enriching the system with high-quality human feedback.

Architectural Consequence

Traditional architectures treat human review as a process. LLM architectures treat it as infrastructure.

This requires:

• Integrated review interfaces (dashboards or tools for humans to approve, correct, or rate outputs)

• Feedback ingestion APIs that flow reviewed data back into analytics, evaluation, or training

• Workflow engines that handle escalation, branching logic, and thresholds

• Audit logs for compliance and accountability

• Confidence scoring mechanisms to trigger human intervention when needed

In other words, human oversight is no longer an afterthought or a business rule—it becomes a first-class architectural layer that maintains safety, correctness, and user trust.

LLM Systems Change Even When You Don’t Touch Them

In classical software engineering, change is tied to deliberate developer actions. You ship new code → the system changes. Between releases, the system is stable and predictable. But LLM-powered applications break this pattern completely. Their behavior evolves continuously—even when no one is “deploying” anything. This shift fundamentally alters how we think about change management.

Multiple factors cause ongoing behavioral drift:

• Data Updates: New documents, updated facts, or changes in retrieval indexes can alter the model’s answers without any code changes.

• Model Updates: Vendors like OpenAI or Anthropic can roll out quiet updates, improving safety, reasoning, or performance, which subtly shift output behavior.

• Prompt Changes: Tiny modifications to prompts, templates, or context windows can result in noticeably different outputs.

• External World Shifts: LLMs depend on real-world knowledge. When laws change, policies evolve, or market conditions shift, the model’s “correct” output changes too.

• Vendor Model Upgrades: Migrating from GPT-4.1 to GPT-5.1 or Claude 3.7 can improve capability but break downstream behaviors, especially prompt-dependent logic.

In other words, LLM behavior is dynamic, not static. This introduces continuous, ambient change that must be managed at the architectural level.

Architecture Must Support Continuous Evolution

To maintain reliability and prevent unexpected regressions, LLM architectures need built-in mechanisms to test, evaluate, and control behavioral changes.

Key capabilities include:

• Side-by-Side Model Versions: Run new and old models simultaneously to compare reasoning, safety profiles, and output quality before switching.

• Canary Testing: Roll out model updates to a small subset of users or queries to detect performance drops before broad deployment.

• A/B Evaluation: Evaluate prompts, RAG configurations, or safety filters in parallel to gather objective quality metrics.

• Rollback Mechanisms: Systems must be able to revert the following:

• Prompts

• RAG configurations

• Model versions

• Embedding models

• Reference datasets

This is drastically more complex than simply rolling back code; it may involve reverting multiple layers across the AI stack.

Architectural Consequence

In traditional software, you version code. In LLM systems, you version everything:

• Models

• Prompts

• Embeddings

• Datasets

• Safety policies

• Vector indexes

• Evaluation scores

• Behavioral baselines

Each component can change independently, and each change can influence system behavior.

This means architecture must implement:

• Version-controlled prompts and data

• Reproducible pipelines for embedding and RAG rebuilding

• Model registries with lineage tracking

• Behavioral regression suites

• Rollback plans for both the model and data drift

In short, change management becomes a continuous cycle rather than a release process.

LLM Architecture Is a New Engineering Discipline

LLM-powered applications require more than new tools—they demand a fundamentally different way of thinking about architecture. Traditional software is built on deterministic logic that you can precisely specify, control, and test. LLM systems, by contrast, operate in a probabilistic space where behaviors emerge from data, prompts, and context, and where models evolve continuously.

This means architecture is no longer about orchestrating fixed logic; it’s about shaping, constraining, grounding, and evaluating behavior. Instead of asking, “How do we implement this rule?”, we begin asking, “How do we guide this system into producing reliable, safe, and consistent results?”

Architecture Must Solve for a New Set of Quality Realities

To operate reliably, LLM systems must be architected around challenges that traditional systems seldom face:

• Data quality: Your model’s behavior is only as sound as the data it ingests; training data, retrieval data, fine-tuning data, and real-time user input all shape outcomes.

• Model unpredictability: Behaviors shift with prompt changes, model updates, data refreshes, and external world evolution, requiring architectures that can absorb variability.

• Safety risks: Toxicity, hallucinations, misalignment, and subtle biases demand active mitigation, not static safeguards.

• Continuous evolution: Unlike code-based systems, LLMs naturally drift. Architecture must support monitoring, versioning, canary releasing, rollback, and fine-grained evaluation.

• Human oversight: When the stakes are high, humans aren’t optional—they’re part of the architecture. Approval workflows and escalation paths become system components.

• Complex evaluation cycles: Testing moves from binary correctness to continuous behavioral assessment, through golden sets, scenario tests, fairness audits, adversarial probing, and drift detection.

It’s Not Harder, It’s Just Different

LLM engineering isn’t inherently more difficult; it’s simply operating under new rules. The foundational challenges—data quality, safety, governance, scalability, and iteration—are familiar, but the way they manifest is dramatically different.

Organizations that cling to deterministic assumptions will struggle with instability, drift, and unpredictable failures. Those that embrace LLM-centric architectures—rooted in feedback loops, evaluation layers, safety guardrails, and data-first design—will build systems that scale reliably, responsibly, and competitively.

As companies move from prototypes to production, these architectural principles aren’t “nice to have”—they’ll determine which systems deliver enduring value and which collapse as complexity grows.