AI Evaluation Architecture Diagrams

Visualize LLM evaluation (evals) pipelines — offline test dataset management, online production scoring, LLM-as-judge pipelines, CI/CD integration, and evaluation platforms like BrainTrust, LangSmith, and Arize Phoenix. Generate accurate AI eval architecture diagrams from plain English in seconds.

What is an AI evaluation architecture?

An AI evaluation architecture is the system infrastructure that measures the quality and safety of LLM outputs — systematically, repeatably, and at scale. It covers: the dataset of test cases used to assess model behavior, the scoring functions (heuristic, model-based, human) applied to each output, the CI/CD gate that blocks a prompt change from reaching production if quality degrades, and the online monitoring layer that detects regressions in live production traffic.

Evals architecture is increasingly recognized as a first-class engineering concern — without it, teams have no objective way to know whether a model update, prompt change, or RAG configuration change improved or regressed the system's behavior.

Key components to diagram

Eval dataset store

The foundation of any eval system is a curated dataset of (input, expected output) pairs covering the scenarios your system must handle correctly. Show the dataset store — typically a database or versioned file (JSON, CSV, YAML) in source control — as the root node of the offline eval pipeline. Include the version control layer so that dataset changes are tracked alongside code changes.

Scoring layer

Each eval run applies one or more scorers to assess output quality. Diagram the scoring layer as a set of parallel nodes: deterministic scorers (exact match, regex, JSON schema validation), semantic scorers (embedding cosine similarity), and LLM-as-judge scorers (a separate model — typically a larger, more capable model — that evaluates the output against a rubric). Show the aggregation step that combines individual scores into a composite pass/fail result.

CI/CD eval gate

The eval gate integrates the offline eval suite into the CI/CD pipeline. When a developer submits a PR changing a prompt, RAG configuration, or model version, the eval suite runs automatically. If the composite score drops below a configurable threshold, the PR is blocked. Show the gate as a decision node in the CI/CD flow with two edges: pass (deploy) and fail (block with score report).

Online monitoring

Online evals run continuously on a sample of production traffic. A sampling layer selects N% of live requests, sends them (with their responses) to an async scoring pipeline, and writes scores to a time-series store. Alerts fire when rolling score averages drop below baseline. Show the sampling step as a branch off the main request path, feeding into the scoring pipeline and dashboard.

Evaluation platforms (2026)

BrainTrust: Managed eval platform with dataset versioning, experiment tracking, LLM-as-judge, and CI/CD integration
LangSmith: LangChain's observability and eval platform — trace logging, dataset management, and annotation workflows
Arize Phoenix: Open-source LLM observability with built-in evals, embedding visualization, and retrieval analysis for RAG
PromptFoo: Open-source CLI eval runner — runs evals from YAML config, integrates with CI/CD, supports custom scorers
Confident AI: DeepEval-based managed platform for unit testing LLM outputs against metrics like faithfulness, relevancy, and hallucination

Example prompt

"LLM evaluation pipeline for a customer support RAG system. Offline eval suite: 250 golden (question, expected answer) pairs stored in a PostgreSQL eval dataset table, versioned by Git tag. CI/CD: on every PR that changes the system prompt or retrieval config, GitHub Actions triggers an eval run via BrainTrust SDK. Each output is scored by three scorers in parallel: (1) faithfulness scorer — GPT-4o checks whether the answer is grounded in the retrieved context; (2) answer relevancy scorer — cosine similarity between the answer embedding and the question embedding (threshold 0.82); (3) policy compliance scorer — regex checks for prohibited phrases (competitor names, pricing guarantees). PR is blocked if aggregate score falls below 0.85. Online monitoring: 5% of live requests are sampled asynchronously and scored by the faithfulness scorer only (cost constraint). Scores stored in InfluxDB; Grafana alert fires if 24-hour rolling average drops below 0.80. Show offline and online paths as separate branches from the main system diagram."

Frequently asked questions

What is LLM-as-judge evaluation?

LLM-as-judge is an evaluation technique where a separate, typically more capable language model scores the output of the system under test. The judge model is given the input, the system's output, and optionally a reference answer or rubric, then asked to score the output on dimensions like correctness, faithfulness, or safety. LLM-as-judge scales better than human annotation and correlates well with human judgments when the judge model is significantly more capable than the system being evaluated.

What is the difference between offline and online evals?

Offline evals run against a fixed dataset of test cases — they are deterministic, cheap to run, and integrated into CI/CD to catch regressions before deployment. Online evals run against live production traffic on a sampled basis — they catch distribution shift, edge cases not covered by the test dataset, and user behavior patterns that weren't anticipated during development. Both layers are necessary: offline for release gates, online for continuous production monitoring.

How do I integrate evals into a CI/CD pipeline?

The standard pattern is: (1) store your eval dataset and scoring thresholds in source control; (2) add a CI/CD step (GitHub Actions, CircleCI) that runs your eval suite on every PR touching prompts, RAG config, or model version; (3) the step fails the build if the composite score drops below your threshold; (4) results are posted as a comment on the PR with a score breakdown. Tools like BrainTrust, PromptFoo, and Confident AI have native CI/CD integrations via SDK or CLI commands.

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