LLM evaluations measure whether your AI system is accurate, safe, and useful. This guide explains the four core evaluation types, LLM-as-Judge, Programmatic Rules, Human-in-the-Loop, and Composite Evaluations, when to use each, and how to b
César Miguelañez
Feb 13, 2026
LLM evaluations measure whether your AI system produces outputs that are correct, safe, and useful. Choosing the right evaluation type determines whether you catch real failures or waste time on metrics that don't matter. The four principal types—LLM-as-Judge, Programmatic Rules, Human-in-the-Loop, and Composite Evaluations—each solve different problems and work best in different situations.
Most teams default to one evaluation type and apply it everywhere. This creates blind spots. A programmatic rule can't assess whether a response sounds helpful. A human reviewer can't scale to thousands of daily requests. Understanding when to use each type lets you build evaluation systems that actually improve reliability.
What are LLM evaluations?
LLM evaluations are systematic methods for assessing the quality, accuracy, and safety of outputs from large language models. They answer the question: "Did the model do what we wanted?"
Unlike traditional software testing, LLM evaluations must handle probabilistic outputs where the same input can produce different valid responses. This makes evaluation design more nuanced than simple pass/fail assertions.
Effective LLM evaluation combines multiple methods to cover different quality dimensions. No single evaluation type captures everything that matters.
The four types of LLM evaluations
There are four principal types of LLM evaluations, each with distinct strengths:
LLM-as-Judge: Uses another language model to assess output quality
Programmatic Rules: Automated checks using code-based logic
Human-in-the-Loop: Manual review and scoring by domain experts
Composite Evaluations: Combines two or more evaluation types into an aggregate score
Each type excels in specific scenarios. The key is matching the evaluation method to what you're actually trying to measure.
When to use LLM-as-Judge evaluations
LLM-as-Judge evaluations use a language model to assess another model's outputs. This approach works best when you need to evaluate subjective qualities at scale.
Best use cases for LLM-as-Judge
Tone and style assessment: When you need to verify that responses match a specific voice, maintain professionalism, or avoid certain language patterns. A judge model can assess whether a customer service response sounds empathetic or dismissive far better than any rule-based system.
Relevance scoring: When you need to determine whether a response actually answers the user's question. Judge models can understand semantic relationships that keyword matching misses entirely.
Coherence and clarity: When you need to evaluate whether responses are well-structured and easy to understand. This requires comprehension that only another language model can provide.
Comparative evaluation: When you need to determine which of two responses is better. Judge models can weigh multiple factors simultaneously and provide reasoning for their decisions.
When LLM-as-Judge falls short
LLM-as-Judge evaluations struggle with factual accuracy. The judge model may not know whether a specific claim is true. They also inherit biases from their training data, which can lead to systematic blind spots.
Avoid LLM-as-Judge when:
You need to verify specific facts against a known source
The evaluation criteria can be expressed as explicit rules
You require deterministic, reproducible results
Cost per evaluation is a significant constraint
Implementation considerations
Judge models require careful prompt engineering. A vague evaluation prompt produces inconsistent scores. Define explicit criteria, provide examples of good and bad outputs, and test the judge against cases where you already know the correct answer.
When to use Programmatic Rule evaluations
Programmatic Rule evaluations use code-based logic to automatically check outputs against defined criteria. They run instantly, cost nothing per execution, and produce perfectly reproducible results.
Best use cases for Programmatic Rules
Format validation: When outputs must follow a specific structure—JSON schemas, required fields, character limits, or specific formatting patterns. A programmatic check catches these failures instantly and reliably.
Safety filters: When you need to detect prohibited content, PII exposure, or specific harmful patterns. Regular expressions and keyword lists can catch known dangerous outputs before they reach users.
Factual verification against known data: When you can check claims against a database or knowledge base. If the model says a product costs $99, you can verify that against your actual pricing data.
Response length and structure: When outputs must meet specific length requirements or include certain sections. These constraints are trivial to check programmatically.
Latency and cost thresholds: When you need to flag responses that exceeded acceptable token usage or response times. These operational metrics are inherently numeric.
When Programmatic Rules fall short
Programmatic rules cannot assess quality dimensions that require understanding. They miss subtle errors, can't evaluate whether a response is actually helpful, and fail when the criteria can't be expressed as explicit logic.
Avoid Programmatic Rules when:
Quality depends on subjective judgment
Valid outputs can take many different forms
You're evaluating reasoning or explanation quality
The failure modes are semantic rather than structural
Implementation considerations
Start with the failures you can define precisely. If you can write a clear rule for what constitutes failure, you should implement it programmatically. These evaluations form the foundation of any robust evaluation system because they catch obvious problems cheaply.
When to use Human-in-the-Loop evaluations
Human-in-the-Loop evaluations involve domain experts manually reviewing and scoring model outputs. This approach captures nuance that automated methods miss but comes with significant cost and scale constraints.
Best use cases for Human-in-the-Loop
Domain expertise requirements: When correct evaluation requires specialized knowledge—medical accuracy, legal compliance, or technical correctness in a specific field. Only qualified humans can assess whether a response meets professional standards.
Edge case investigation: When you need to understand why specific failures occur. Human reviewers can identify patterns and provide qualitative feedback that automated systems cannot generate.
Ground truth creation: When you need to build labeled datasets for training other evaluation methods. Human judgments become the reference standard that LLM-as-Judge and programmatic rules learn from.
High-stakes decisions: When the consequences of incorrect outputs are severe enough to justify manual review. Regulated industries often require human oversight regardless of automated evaluation results.
Evaluation calibration: When you need to verify that your automated evaluations correlate with actual quality. Periodic human review keeps automated systems honest.
When Human-in-the-Loop falls short
Human evaluation doesn't scale. A team can review hundreds of outputs per day, not thousands. Human reviewers also introduce their own inconsistencies—different reviewers may score the same output differently, and individual reviewers may drift over time.
Avoid Human-in-the-Loop as your primary method when:
You need to evaluate every production request
Consistency across evaluators is critical
The evaluation criteria are straightforward enough for automation
Speed of feedback matters for iteration
Implementation considerations
Design clear rubrics with specific criteria and examples. Train reviewers together to calibrate their judgments. Use human evaluation strategically—for building training data, investigating failures, and validating automated methods—rather than as the primary evaluation mechanism.
When to use Composite Evaluations
Composite Evaluations combine two or more evaluation types into an aggregate score. This is the recommended default for most production AI systems because real-world quality has multiple dimensions that no single evaluation type can capture.
Why composite evaluations matter
Most AI tasks involve tradeoffs. A response might be factually accurate but poorly written. Another might be engaging but miss key information. Single evaluations optimize for one dimension while potentially degrading others.
Composite evaluations prevent overfitting to narrow metrics. When you optimize against a single evaluation, the model learns to game that specific measure. Multiple evaluations create a more robust quality signal that's harder to exploit.
Best use cases for Composite Evaluations
Production quality monitoring: When you need a single score that reflects overall quality across multiple dimensions. A composite score combining accuracy, safety, and helpfulness gives a more complete picture than any individual metric.
Prompt optimization: When you're automatically improving prompts based on evaluation feedback. Composite evaluations ensure that improvements in one area don't cause regressions in others.
Complex task assessment: When the task involves multiple success criteria—a customer service bot needs to be accurate, helpful, appropriately toned, and safe. Each dimension requires different evaluation approaches.
Balanced improvement: When you want to improve systematically without sacrificing existing strengths. Composite scores surface tradeoffs that single metrics hide.
How to construct composite evaluations
Start by identifying the distinct quality dimensions that matter for your use case. For each dimension, choose the evaluation type best suited to measure it:
Use Programmatic Rules for format compliance, safety filters, and verifiable facts
Use LLM-as-Judge for tone, relevance, and coherence
Use Human-in-the-Loop for domain expertise and ground truth validation
Weight each component based on its importance to your users and business. A safety violation should typically have higher weight than a minor tone issue.
Implementation considerations
Keep composite evaluations interpretable. When the aggregate score drops, you need to identify which component caused the decline. Log individual component scores alongside the composite to enable debugging.
Review component weights periodically. As your system matures and certain failure modes become rare, you may need to rebalance the evaluation to focus on remaining problems.
How to choose the right evaluation type
Selecting evaluation types requires matching your quality requirements to evaluation capabilities. Start with these questions:
Can you define failure precisely?
If yes, implement a Programmatic Rule. These are the cheapest and most reliable evaluations. Every failure mode you can express as code should be checked programmatically.
Does assessment require understanding language?
If you need to evaluate meaning, relevance, or quality of expression, use LLM-as-Judge. This covers most subjective quality dimensions at scale.
Does assessment require specialized expertise?
If correct evaluation requires domain knowledge that models lack, use Human-in-the-Loop. This is essential for regulated industries and specialized technical domains.
Do multiple quality dimensions matter?
If yes, build a Composite Evaluation. This is the recommended approach for any production system where more than one thing matters.
Best practices for LLM evaluation systems
Start with programmatic rules. They're free, fast, and deterministic. Build a foundation of structural and safety checks before adding more sophisticated evaluations.
Use human evaluation strategically. Don't try to review everything. Use human judgment to build training data, investigate failures, and calibrate automated methods.
Test your evaluations. An evaluation that doesn't correlate with actual quality is worse than no evaluation. Validate that your metrics reflect what users actually care about.
Monitor evaluation stability. LLM-as-Judge evaluations can drift as judge models update. Track evaluation distributions over time and investigate unexpected shifts.
Build composite evaluations early. Single metrics create blind spots. Even a simple composite of two or three evaluations provides better signal than optimizing for one dimension.
Evaluation types and the reliability loop
Evaluations are not a one-time setup. They're part of a continuous improvement cycle where production data feeds back into system refinement.
The reliability loop works like this: you run experiments to observe AI behavior, annotate outputs to identify what's working and what's failing, discover patterns in the failures, build automated evaluations to track those patterns, and iterate on prompts or models to improve.
Each evaluation type plays a role in this loop. Programmatic rules catch known failure modes automatically. LLM-as-Judge scales quality assessment across all production traffic. Human reviewers discover new failure patterns and create ground truth for automation. Composite evaluations ensure that improvements are balanced and sustainable.
Teams that build robust evaluation systems don't just catch more failures. They improve faster because they have clear signals about what's working and what needs attention.
Getting started with LLM evaluations
Effective LLM evaluation requires the right infrastructure. You need instrumentation to capture inputs and outputs, storage for evaluation results, and interfaces for human review.
Latitude provides a complete evaluation platform with support for all four evaluation types. The platform integrates LLM-as-Judge, Programmatic Rules, and Human-in-the-Loop evaluations into a unified system where you can build composite evaluations, track quality over time, and feed results into prompt optimization.
For teams building production AI systems, evaluation isn't optional—it's the mechanism that turns unreliable prototypes into products users can trust.
Frequently Asked Questions
What is the difference between LLM-as-Judge and Human-in-the-Loop evaluations?
LLM-as-Judge uses another language model to assess outputs and can scale to thousands of evaluations per minute at low cost. Human-in-the-Loop uses domain experts who provide higher-quality judgments but cannot scale beyond hundreds of reviews per day. Most teams use LLM-as-Judge for continuous monitoring and Human-in-the-Loop for calibration and edge case investigation.
When should I use a composite evaluation instead of a single evaluation type?
Use composite evaluations whenever more than one quality dimension matters for your use case. This is the recommended default for production systems because single evaluations create blind spots and encourage overfitting. Composite evaluations combine multiple signals into a balanced quality score that reflects overall system performance.
Can programmatic rules replace LLM-as-Judge evaluations?
Programmatic rules and LLM-as-Judge evaluations serve different purposes. Rules excel at checking format compliance, safety filters, and verifiable facts. LLM-as-Judge excels at assessing subjective qualities like tone, relevance, and coherence. Most robust evaluation systems use both types together, with rules handling structural checks and judge models handling semantic assessment.
How do I know if my LLM evaluations are working correctly?
Validate evaluations by comparing their scores against human judgments on a sample of outputs. If your automated evaluations don't correlate with what humans consider good or bad, the metrics aren't measuring real quality. Periodic human review keeps automated evaluations calibrated and surfaces drift over time.
