Why One Evaluation Type Isn't Enough: Different Questions Require Different Methods

The most common mistake in AI evaluation is assuming that one evaluation method works for all questions. It doesn't. Different types of eval answer different questions:

Using the wrong eval type gives wrong answers. If you test code generation solely with human reviewers, you miss logic errors. If you test marketing copy solely with automated metrics, you miss persuasiveness. If you test only on benchmarks, you miss production realities.

The path to good evaluation is knowing which type answers each question.

Type 1: Automated Evaluation—Speed and Scale, With Blindspots

What It Is

Automated evaluation means computational scoring of AI outputs without human input. The evaluation is deterministic, fast, and scalable. You can evaluate millions of outputs.

The Three Categories of Automated Evaluation

1. Rule-Based Evaluation

Hard rules that outputs must follow. Either the output satisfies the rule or it doesn't.

Examples:

Pros: Deterministic, no subjectivity, extremely fast

Cons: Only works for rule-checkable properties, misses subtle quality issues

2. Statistical Metrics

Numerical scoring based on reference comparison. Compare generated output to "correct" reference output.

Examples:

Pros: Deterministic, reference-based (grounded), scalable

Cons: Weak correlation with human quality, penalizes novel solutions, often meaningless

3. Model-Based Evaluation

Using another trained model to score outputs. LLM judges are the canonical example: ask a large language model to score another model's output.

Examples:

Pros: Interpretable scoring, captures soft qualities, scalable

Cons: Biased toward the judge model's quirks, may not correlate with human preference, expensive

When to Use Automated Evaluation

  • High-volume evaluation: Millions of outputs, need fast feedback
  • Continuous monitoring: Need to evaluate every production request
  • Rule-checkable properties: Output must satisfy hard constraints
  • Development iteration: Quick feedback loop during model training
  • Cost-driven: Human evaluation budget is exhausted

The Automated Evaluation Blindspots

Type 2: Human Evaluation—Ground Truth, But Expensive and Slow

What It Is

Humans (annotators, raters, experts) manually review AI outputs and provide quality judgments. This is how you get ground truth.

The Two Categories of Human Evaluation

1. Annotation-Based Evaluation

Annotators label outputs according to predefined criteria. This creates training data and ground truth.

Common scenarios:

Pros: Captures human judgment, ground truth for training, high quality

Cons: Expensive ($0.50-5.00 per annotation), slow (days to weeks), requires quality management

2. Expert Evaluation

Domain experts (lawyers, doctors, engineers) review outputs and provide nuanced assessment. Higher quality than general annotators, much more expensive.

Common scenarios:

Pros: Highest quality assessment, understands domain nuances, catches subtle errors

Cons: Very expensive ($50-500 per evaluation), slow, hard to scale

When to Use Human Evaluation

  • High-stakes decisions: Errors have serious consequences
  • Subjective quality: Output quality is hard to define algorithmically
  • Ground truth creation: Building training data or benchmark
  • Expert validation: Decisions require domain expertise
  • One-time assessment: Evaluating one-off models or versions

The Human Evaluation Challenges

Type 3: Hybrid Evaluation—The Best of Both Worlds

What It Is

Combining automated and human evaluation. Use automated methods for scale and speed, human evaluation for validation and quality control. This is the most practical approach for production systems.

The Hybrid Strategy

Step 1: Automated Pre-Filtering

Run automated evaluation on all outputs. Flag only the ones that failed or have low confidence scores for human review.

Example: "Run toxicity classifier on all 100M monthly responses. 99.9% pass automatically. Send 100k (0.1%) flagged responses to human raters."

Step 2: Human Deep Dive on Sample

Have humans review a representative sample of all outputs (both passing and failing automated eval) to validate the automated scoring.

Example: "Sample 500 toxicity-flagged responses and 500 toxicity-passed responses. Have humans rate each. Measure agreement between automated toxicity classifier and human judgment."

Step 3: Iterative Improvement

Use human labels to improve automated evaluation. Retrain the classifier. Reduce false positives and false negatives over time.

Result: Automated evaluation that's calibrated to human standards, at scale, with continuous improvement.

When Hybrid Is Ideal

Type 4: Observational Evaluation—Real-World Signal

What It Is

Using production behavior data as eval signal. Did users like it? Did they use it again? Did they give it positive feedback? Did it drive business outcomes?

The Two Categories of Observational Signals

1. Explicit Signals

Users directly tell you if something is good.

Examples:

Pros: Direct signal of user preference, real-world outcome

Cons: Low response rate (typically 1-5% of users rate), biased toward extreme responses (very happy or very upset), slow to collect

2. Implicit Signals

Users indirectly demonstrate preference through behavior.

Examples:

Pros: Automatic collection, no bias from non-responders, real behavior signal

Cons: Confounded by other factors, correlation doesn't imply causation, indirect signal

When to Use Observational Evaluation

  • Production validation: Want to validate that changes help real users
  • A/B testing: Comparing two versions of the system
  • Long-term impact: Measuring sustained user value
  • Business outcomes: Tying AI quality to revenue, retention, or engagement
  • Continuous monitoring: Tracking quality degradation in production

The Observational Evaluation Challenges

Choosing the Right Evaluation Type: A Decision Framework

Use this decision framework to select the right eval type for your situation:

Question 1: What Quality Dimension Are You Evaluating?

Hard constraints (code compiles, JSON is valid): → Automated rule-based

Quantifiable metrics (latency, throughput): → Automated statistical or rule-based

Soft qualities (tone, helpfulness, persuasiveness): → Human or hybrid

Expert judgment (medical correctness, legal validity): → Human expert

Real-world impact (user satisfaction, business outcome): → Observational

Question 2: What Scale Do You Need?

Millions of outputs/month: → Automated or hybrid (not purely human)

Thousands of outputs: → Hybrid (automated + human sample)

Hundreds of outputs: → Human or hybrid (can afford human review on all)

Tens of outputs: → Human expert

Question 3: What's Your Budget?

Budget: $100k-1M/year: → Hybrid (automated + targeted human eval)

Budget: $10k-100k/year: → Automated or light hybrid

Budget: <$10k/year: → Automated only

Budget: >$1M/year: → All types; comprehensive evaluation program

Question 4: What Are the Stakes?

Low stakes (recommendation, content moderation): → Automated primary, human validation

Medium stakes (customer support, document categorization): → Hybrid (automated + human sample)

High stakes (medical, legal, financial): → Human expert primary, automated secondary

Critical stakes (life-and-death decisions): → Expert human, not AI-driven

Question 5: How Quickly Do You Need Feedback?

Real-time (seconds): → Automated only

Fast (minutes/hours): → Automated with async human validation

Normal (days/weeks): → Hybrid or human

Slow (weeks/months): → Observational or comprehensive human eval

Decision Tree

If stakes are high: Use human evaluation, especially experts. Cost is justified.

If scale is huge: Use hybrid (automated + human sampling). Pure human is impossible.

If you need ground truth: Use human annotation. Automated can't create training data.

If you need real-world validation: Use observational. Benchmarks don't predict everything.

If you have time: Use all four types. Triangulation reduces blindspots.

Combining Evaluation Types: Triangulation Strategy

The best evaluation programs use all four types, each validating the others. Here's how triangulation works:

The Validation Loop

Step 1: Automated Evaluation Run automated eval on all outputs. Get a signal fast.

Step 2: Human Evaluation (Sample) Sample 500-1000 outputs (both passing and failing automated eval). Have humans rate each. Compare to automated scores.

Step 3: Hybrid Calibration If human and automated scores disagree, investigate why. Adjust automated eval thresholds or add new rules.

Step 4: Observational Validation Roll out a small subset to production with observational metrics enabled. Do users actually like what automated+human eval said was good?

Step 5: Full Rollout If observational metrics are positive, roll out to full production. Continue monitoring observational signals.

Using Eval Types to Resolve Conflicts

Scenario: Automated eval says output is good (99% confidence), human rater says it's poor.

Investigation: Why do they disagree? Possibilities:

Resolution: Use observational data (real users) to break the tie. If real users like it, trust the automated eval. If they don't, the human rater was right.

Type Selection by Use Case: A Practical Matrix

Use Case Primary Type Secondary Type Validation Type
Code generation Automated (test execution) Human (expert engineer) Observational (developer adoption)
Recommendation system Observational (click-through, conversion) Hybrid (automated + human sample) Human (periodic fairness audit)
Content moderation Automated (toxicity classifier) Human (safety reviewer sample) Observational (user appeal rate)
Legal AI Human (expert attorney) Automated (rule checking) Observational (attorney adoption)
Customer service chatbot Observational (issue resolution) Hybrid (automated + human sample) Automated (first-contact resolution)
Search ranking Observational (click-through, dwell) Automated (relevance score) Human (relevance raters)
Translation Human (fluency + accuracy) Automated (statistical metrics) Observational (user satisfaction)
Summarization Hybrid (automated ROUGE + human) Human (summary quality) Observational (user reads summary)
Question answering Hybrid (automated + human sample) Observational (user satisfaction) Human (expert validation)
Image generation Human (quality rating) Automated (CLIP score) Observational (user preference)

Failure Modes by Eval Type: What Each Type Misses

Every eval type has blindspots. Understanding what each type misses helps you use them together effectively.

Automated Evaluation Failure Modes

Human Evaluation Failure Modes

Hybrid Evaluation Failure Modes

Observational Evaluation Failure Modes

73%
Of AI teams use only one eval type
21%
Use two types (usually automated + human)
6%
Use all four types (best practice)
2x
More likely to catch problems with multi-type eval

Building a Multi-Type Eval Program: Resource Allocation and Program Design

The Budget Allocation Model

For a typical AI evaluation program with $500k annual budget, allocate like this:

Typical Cost Ratios

Eval Type Cost Per Item Suitable Scale Annual Budget Needed
Automated Rule-Based $0.0001 10M+ items/year $1-10k infrastructure
Automated Model-Based $0.01-0.10 1-10M items/year $10-100k infrastructure
General Annotator $0.50-2.00 10k-100k items/year $5-200k labor
Expert Rater $50-500 10-1k items/year $5-500k labor
Observational (free post-launch) $0 After launch only Infrastructure cost only

Multi-Type Eval Program Structure

Phase 1: Development (Model in training)

Phase 2: Validation (Model ready for production test)

Phase 3: Production (Model in production)

Summary: Choose Your Eval Types Strategically

There is no one-size-fits-all evaluation type. Each has distinct strengths and blindspots:

Automated evaluation: Fast, scalable, deterministic. Misses subjective quality and novel solutions.

Human evaluation: Ground truth, high quality. Expensive and slow.

Hybrid evaluation: Best of both. Requires maintaining multiple systems.

Observational evaluation: Real-world signal. Slow and confounded.

The winning strategy: Use all four types. Automated for speed and scale. Human for ground truth. Hybrid for production. Observational for validation. Each type validates the others. Together, they catch what each misses individually.