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
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.
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
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
Establish Metrics
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.