Evaluation of LLM Applications

#Critical decisions

• model selection
• prompts
• data augmentation
• performance monitoring.

A key concern is understanding how a model performs within the application’s context and how well it meets anticipated usage patterns.

Building a Robust Evaluation Dataset

• Manual Seeding: Start by manually crafting data points using domain expertise to ensure relevance and diversity.
• Continuous Feedback Loop: Enrich the dataset with real user interactions and production logs as your application evolves.
• Synthetic Data: Leverage LLMs to generate synthetic edge cases and underrepresented scenarios.

Establishing precise criteria that reflect real-world outcomes is essential for meaningful evaluation.

Key Evaluation Principles for LLM-Powered Applications

1. Data is the Foundation
   Create a dataset that mirrors actual application usage. Continuously improve it using feedback loops. Remember:

   Garbage in, garbage out — evaluation quality is only as good as the data it’s based on.

2. Evaluate Beyond the Model
   LLMs operate as part of a system. Evaluation should include:

   • Prompt engineering strategies
   • Data augmentation methods
   • Intermediate reasoning steps
   • End-to-end workflow evaluation

3. Precisely Define “Good”
   Avoid generic metrics. Instead:

   • Define success criteria tailored to your use case
   • Use task-specific rubrics for more effective feedback

Evaluation Strategies

Evaluation Types

• Point-wise Evaluation: Rate individual model outputs.
• Pair-wise Evaluation: Compare two outputs and decide which is better.

Step-by-Step Evaluation Pipeline

1. Establish Metrics

2. Design Rubrics

   • Rating (Quantitative)
   • Rationale (Qualitative)

Automated vs Human Evaluation

Computation-Based Metrics

• Lexical Similarity
  • ROUGE (Recall-Oriented Understudy for Gisting Evaluation)
  • BLEU (Bilingual Evaluation Understudy)
• Embedding Similarity
  • BERT, BART embedding comparisons

Human-in-the-Loop Evaluation

Autoraters and Meta-Evaluation

LLM-based autoraters can be used but need bias calibration. Techniques include:

• Prompt Engineering & Orchestration: Design precise evaluation prompts.
• Swapping Positions: Mitigate position bias by randomizing response order.
• Self-Consistency Checks: Generate multiple outputs for the same input.
• Model Juries: Use a panel of diverse models to reduce individual bias.
• De-biasing Datasets: Fine-tune autoraters on curated evaluation data.

Example Enum

class SummaryRating(enum.Enum):
    VERY_GOOD = '5'
    GOOD = '4'
    OK = '3'
    BAD = '2'
    VERY_BAD = '1'

structured_output_config = types.GenerateContentConfig(
    response_mime_type="text/x.enum",
    response_schema=SummaryRating,
)

Pairwise Evaluation Prompt Example

QA_PAIRWISE_PROMPT = """# Instruction
You are an expert evaluator. Your task is to evaluate the quality of responses from two AI models.

# Evaluation
## Metric Definition
Evaluate based on instruction following, groundedness, completeness, and fluency.

## Rating Rubric
"A" - Response A is better  
"B" - Response B is better  
"SAME" - Both are equal

## Evaluation Steps
1. Analyze Response A
2. Analyze Response B
3. Compare both responses
4. Output "A", "B", or "SAME"
5. Provide explanation for your choice

# User Prompt
{prompt}

# Response A
{baseline_model_response}

# Response B
{response}
"""

Final Thoughts

Evaluation of LLM systems is multi-dimensional. Good metrics, data quality, prompt design, and monitoring mechanisms all contribute to making systems reliable, interpretable, and aligned with end-user goals.