⚙️ Session 5: Advanced Custom Metrics¶
⚙️ IMPLEMENTER PATH - Advanced Custom Evaluation Prerequisites: Complete 🎯 Observer and 📝 Participant paths Time Investment: 3-4 hours Outcome: Build sophisticated domain-specific RAG evaluators
Learning Outcomes¶
By completing this section, you will master:
- Domain-specific evaluation metric design and implementation
- Advanced LLM-as-a-Judge evaluation systems
- Sophisticated retrieval quality assessment techniques
- Custom evaluation frameworks for specialized RAG applications
Prerequisites Validation¶
Before diving into advanced implementations, ensure mastery of:
- Completed 🎯 RAG Evaluation Essentials
- Completed 📝 RAGAS Implementation Practice
- Working knowledge of statistical analysis and evaluation theory
- Experience with production RAG system deployment
⚙️ Advanced Custom Metrics Framework¶
Domain-Specific Evaluation Architecture¶
Standard metrics like RAGAS provide excellent baseline evaluation, but production RAG systems often require specialized metrics that capture domain-specific quality dimensions. Let's build a sophisticated framework for custom evaluation:
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Optional, Tuple
import numpy as np
from dataclasses import dataclass, field
import json
import re
from collections import defaultdict
@dataclass
class CustomEvaluationResult:
"""Comprehensive result structure for custom evaluations."""
metric_name: str
score: float
confidence: float
explanation: str
component_scores: Dict[str, float] = field(default_factory=dict)
evidence: List[str] = field(default_factory=list)
improvement_suggestions: List[str] = field(default_factory=list)
metadata: Dict[str, Any] = field(default_factory=dict)
class AdvancedMetricFramework(ABC):
"""Abstract base for advanced custom evaluation metrics."""
def __init__(self, name: str, llm_judge=None, domain_knowledge=None):
self.name = name
self.llm_judge = llm_judge
self.domain_knowledge = domain_knowledge or {}
self.evaluation_history = []
@abstractmethod
def evaluate(self, query: str, response: str, contexts: List[str],
**kwargs) -> CustomEvaluationResult:
"""Core evaluation method - must be implemented by subclasses."""
pass
def batch_evaluate(self, evaluation_data: List[Dict]) -> List[CustomEvaluationResult]:
"""Efficient batch evaluation with progress tracking."""
results = []
total_items = len(evaluation_data)
for i, item in enumerate(evaluation_data):
if i % 10 == 0:
print(f"Custom evaluation progress: {i+1}/{total_items}")
result = self.evaluate(
item['query'], item['response'],
item.get('contexts', []), **item.get('kwargs', {})
)
results.append(result)
# Store evaluation history for learning
self.evaluation_history.append({
'input': item,
'result': result,
'timestamp': time.time()
})
return results
This framework provides the foundation for building specialized evaluation metrics while maintaining consistency and extensibility.
Advanced Citation Quality Evaluator¶
Let's implement a sophisticated citation quality metric that goes beyond simple pattern matching:
class AdvancedCitationQualityMetric(AdvancedMetricFramework):
"""Advanced evaluation of citation quality and source attribution."""
def __init__(self, llm_judge, domain_knowledge=None):
super().__init__("advanced_citation_quality", llm_judge, domain_knowledge)
# Citation pattern recognition
self.citation_patterns = {
'explicit_reference': [r'\[Source: [^\]]+\]', r'\(Source: [^)]+\)', r'According to [^,.:;]+'],
'implicit_reference': [r'studies show', r'research indicates', r'experts suggest'],
'direct_quote': [r'"[^"]*"', r'\'[^\']*\''],
'paraphrase_indicators': [r'as stated in', r'according to', r'research by']
}
# Quality assessment criteria
self.quality_criteria = {
'specificity': 0.25, # How specific are the citations?
'accuracy': 0.30, # Do citations match source content?
'relevance': 0.25, # Are citations relevant to claims?
'completeness': 0.20 # Are all claims properly attributed?
}
def evaluate(self, query: str, response: str, contexts: List[str],
**kwargs) -> CustomEvaluationResult:
"""Comprehensive citation quality evaluation."""
# Extract citations and analyze patterns
citation_analysis = self._analyze_citation_patterns(response)
# Evaluate citation-context alignment
alignment_score = self._evaluate_citation_alignment(response, contexts)
# Assess citation specificity and quality
specificity_score = self._assess_citation_specificity(citation_analysis)
# Check claim-citation completeness
completeness_score = self._evaluate_claim_coverage(response, citation_analysis)
# Calculate weighted overall score
component_scores = {
'pattern_quality': citation_analysis['quality_score'],
'context_alignment': alignment_score,
'specificity': specificity_score,
'completeness': completeness_score
}
overall_score = sum(
component_scores[criterion] * weight
for criterion, weight in self.quality_criteria.items()
)
return CustomEvaluationResult(
metric_name=self.name,
score=overall_score,
confidence=self._calculate_confidence(component_scores),
explanation=self._generate_explanation(component_scores),
component_scores=component_scores,
evidence=citation_analysis['citations'],
improvement_suggestions=self._generate_improvements(component_scores)
)
This advanced evaluator provides nuanced assessment of citation quality beyond basic pattern matching.
Citation Pattern Analysis Implementation¶
Let's implement sophisticated citation pattern recognition:
def _analyze_citation_patterns(self, response: str) -> Dict[str, Any]:
"""Analyze citation patterns in response text."""
citation_analysis = {
'citations': [],
'pattern_counts': defaultdict(int),
'quality_indicators': [],
'quality_score': 0.0
}
# Extract citations by pattern type
for pattern_type, patterns in self.citation_patterns.items():
for pattern in patterns:
matches = re.findall(pattern, response, re.IGNORECASE)
for match in matches:
citation_analysis['citations'].append({
'text': match,
'type': pattern_type,
'position': response.lower().find(match.lower())
})
citation_analysis['pattern_counts'][pattern_type] += len(matches)
# Assess pattern quality
total_citations = len(citation_analysis['citations'])
if total_citations > 0:
# Reward explicit over implicit references
explicit_ratio = citation_analysis['pattern_counts']['explicit_reference'] / total_citations
citation_analysis['quality_score'] = 0.4 + (explicit_ratio * 0.6)
# Bonus for diverse citation types
pattern_diversity = len([count for count in citation_analysis['pattern_counts'].values() if count > 0])
diversity_bonus = min(pattern_diversity * 0.1, 0.2)
citation_analysis['quality_score'] += diversity_bonus
return citation_analysis
This analysis provides detailed insight into citation patterns and their quality characteristics.
Context-Citation Alignment Assessment¶
Now let's implement sophisticated alignment checking between citations and source contexts:
def _evaluate_citation_alignment(self, response: str, contexts: List[str]) -> float:
"""Evaluate how well citations align with available contexts."""
if not contexts:
return 0.0
# Extract claims and their associated citations
response_sentences = self._extract_sentences_with_citations(response)
alignment_scores = []
for sentence_data in response_sentences:
sentence = sentence_data['text']
has_citation = sentence_data['has_citation']
# Find best matching context for this sentence
best_match_score = 0.0
for context in contexts:
# Calculate semantic similarity (simplified with word overlap)
sentence_words = set(sentence.lower().split())
context_words = set(context.lower().split())
overlap = len(sentence_words.intersection(context_words))
similarity = overlap / max(len(sentence_words), 1)
best_match_score = max(best_match_score, similarity)
# Weight alignment by citation presence
if has_citation and best_match_score > 0.3:
alignment_scores.append(min(best_match_score * 1.2, 1.0)) # Bonus for cited claims
elif not has_citation and best_match_score > 0.3:
alignment_scores.append(best_match_score * 0.8) # Penalty for uncited claims
else:
alignment_scores.append(best_match_score)
return np.mean(alignment_scores) if alignment_scores else 0.0
This alignment assessment ensures citations correspond to actual source content, detecting potential hallucinations.
⚙️ Advanced Retrieval Quality Metrics¶
Multi-Dimensional Retrieval Assessment¶
Standard retrieval metrics focus on relevance, but production systems need comprehensive quality assessment. Let's build advanced retrieval evaluators:
class AdvancedRetrievalQualityMetric(AdvancedMetricFramework):
"""Comprehensive retrieval quality assessment beyond basic relevance."""
def __init__(self, embedding_model, llm_judge=None):
super().__init__("advanced_retrieval_quality", llm_judge)
self.embedding_model = embedding_model
# Multi-dimensional assessment criteria
self.assessment_dimensions = {
'semantic_relevance': 0.30, # How semantically relevant to query
'information_coverage': 0.25, # How much needed info is covered
'source_diversity': 0.20, # Diversity of information sources
'temporal_relevance': 0.15, # Recency and temporal alignment
'authority_quality': 0.10 # Source credibility and authority
}
def evaluate(self, query: str, response: str, contexts: List[str],
**kwargs) -> CustomEvaluationResult:
"""Multi-dimensional retrieval quality evaluation."""
# Semantic relevance assessment
semantic_score = self._calculate_semantic_relevance_advanced(query, contexts)
# Information coverage analysis
coverage_score = self._assess_information_coverage(query, contexts)
# Source diversity evaluation
diversity_score = self._evaluate_source_diversity(contexts)
# Temporal relevance (if temporal info available)
temporal_score = self._assess_temporal_relevance(
query, contexts, kwargs.get('temporal_context')
)
# Authority assessment (if authority info available)
authority_score = self._evaluate_source_authority(
contexts, kwargs.get('source_metadata', {})
)
# Calculate weighted composite score
component_scores = {
'semantic_relevance': semantic_score,
'information_coverage': coverage_score,
'source_diversity': diversity_score,
'temporal_relevance': temporal_score,
'authority_quality': authority_score
}
overall_score = sum(
component_scores[dim] * weight
for dim, weight in self.assessment_dimensions.items()
)
return CustomEvaluationResult(
metric_name=self.name,
score=overall_score,
confidence=self._calculate_retrieval_confidence(component_scores),
explanation=self._generate_retrieval_explanation(component_scores),
component_scores=component_scores,
improvement_suggestions=self._generate_retrieval_improvements(component_scores)
)
This multi-dimensional approach captures the full spectrum of retrieval quality factors that impact RAG system performance.
Advanced Semantic Relevance Calculation¶
Let's implement sophisticated semantic relevance that goes beyond simple similarity:
def _calculate_semantic_relevance_advanced(self, query: str, contexts: List[str]) -> float:
"""Advanced semantic relevance with multiple similarity measures."""
if not contexts:
return 0.0
# Get embeddings for query and all contexts
query_embedding = self.embedding_model.encode([query])[0]
context_embeddings = self.embedding_model.encode(contexts)
relevance_scores = []
for i, context_emb in enumerate(context_embeddings):
# Cosine similarity
cosine_sim = np.dot(query_embedding, context_emb) / (
np.linalg.norm(query_embedding) * np.linalg.norm(context_emb)
)
# Keyword overlap bonus
query_keywords = set(query.lower().split())
context_keywords = set(contexts[i].lower().split())
keyword_overlap = len(query_keywords.intersection(context_keywords))
keyword_bonus = min(keyword_overlap * 0.05, 0.2)
# Context length penalty (very short contexts are often low quality)
length_penalty = 0.0
if len(contexts[i].split()) < 20:
length_penalty = 0.1
# Composite relevance score
composite_score = cosine_sim + keyword_bonus - length_penalty
relevance_scores.append(max(0.0, min(1.0, composite_score)))
# Return weighted average (top contexts weighted more heavily)
sorted_scores = sorted(relevance_scores, reverse=True)
weights = [0.4, 0.3, 0.2, 0.1] if len(sorted_scores) >= 4 else [1.0/len(sorted_scores)] * len(sorted_scores)
weighted_relevance = sum(
score * weight for score, weight in zip(sorted_scores[:4], weights)
)
return weighted_relevance
This advanced calculation provides nuanced relevance assessment that considers multiple quality factors.
Information Coverage Assessment¶
Let's implement sophisticated coverage analysis that determines if retrieved contexts contain sufficient information:
def _assess_information_coverage(self, query: str, contexts: List[str]) -> float:
"""Assess how completely contexts cover information needs."""
# Extract query intent and information needs
information_needs = self._extract_information_needs(query)
coverage_scores = []
for need in information_needs:
need_coverage = 0.0
for context in contexts:
# Check if this context addresses the information need
context_relevance = self._calculate_need_context_relevance(need, context)
need_coverage = max(need_coverage, context_relevance)
coverage_scores.append(need_coverage)
# Overall coverage is the average across all information needs
return np.mean(coverage_scores) if coverage_scores else 0.0
def _extract_information_needs(self, query: str) -> List[str]:
"""Extract distinct information needs from query."""
# Simple information need extraction (in practice, use NLP)
needs = []
# Question word analysis
question_patterns = {
'what': 'definition/explanation',
'how': 'process/method',
'why': 'reason/cause',
'when': 'timing/sequence',
'where': 'location/context',
'who': 'person/entity'
}
query_lower = query.lower()
for pattern, need_type in question_patterns.items():
if pattern in query_lower:
needs.append(need_type)
# If no specific patterns, treat as general information need
if not needs:
needs.append('general_information')
return needs
This coverage assessment ensures retrieved contexts collectively provide comprehensive information to answer the query.
⚙️ Domain-Specific Evaluation Systems¶
Legal Document RAG Evaluator¶
Let's implement a specialized evaluator for legal document RAG systems that considers domain-specific quality requirements:
class LegalRAGEvaluator(AdvancedMetricFramework):
"""Specialized evaluator for legal document RAG systems."""
def __init__(self, llm_judge, legal_knowledge_base=None):
super().__init__("legal_rag_evaluator", llm_judge)
self.legal_knowledge_base = legal_knowledge_base or {}
# Legal-specific evaluation criteria
self.legal_criteria = {
'legal_accuracy': 0.35, # Accuracy of legal information
'citation_completeness': 0.25, # Complete legal citations
'jurisdiction_awareness': 0.20, # Relevant to correct jurisdiction
'precedent_relevance': 0.15, # Relevant legal precedents
'risk_disclosure': 0.05 # Appropriate disclaimers
}
# Legal citation patterns
self.legal_citation_patterns = [
r'\d+\s+\w+\s+\d+', # Case citations (e.g., "123 F.3d 456")
r'\d+\s+U\.S\.C\.\s+§\s+\d+', # USC citations
r'§\s*\d+[\.\d]*', # Section references
]
def evaluate(self, query: str, response: str, contexts: List[str],
**kwargs) -> CustomEvaluationResult:
"""Specialized legal RAG evaluation."""
# Extract case context
jurisdiction = kwargs.get('jurisdiction', 'federal')
case_type = kwargs.get('case_type', 'general')
# Legal accuracy assessment
accuracy_score = self._assess_legal_accuracy(response, contexts)
# Citation completeness check
citation_score = self._evaluate_legal_citations(response)
# Jurisdiction relevance
jurisdiction_score = self._check_jurisdiction_relevance(
response, contexts, jurisdiction
)
# Precedent analysis
precedent_score = self._analyze_precedent_relevance(response, case_type)
# Risk and disclaimer assessment
risk_score = self._evaluate_risk_disclosure(response)
# Composite scoring
component_scores = {
'legal_accuracy': accuracy_score,
'citation_completeness': citation_score,
'jurisdiction_awareness': jurisdiction_score,
'precedent_relevance': precedent_score,
'risk_disclosure': risk_score
}
overall_score = sum(
component_scores[criterion] * weight
for criterion, weight in self.legal_criteria.items()
)
return CustomEvaluationResult(
metric_name=self.name,
score=overall_score,
confidence=self._calculate_legal_confidence(component_scores),
explanation=self._generate_legal_explanation(component_scores),
component_scores=component_scores,
improvement_suggestions=self._generate_legal_improvements(component_scores)
)
This specialized evaluator ensures legal RAG systems meet the stringent requirements of legal information systems.
Medical RAG Evaluator¶
Let's create a specialized evaluator for medical/healthcare RAG systems:
class MedicalRAGEvaluator(AdvancedMetricFramework):
"""Specialized evaluator for medical/healthcare RAG systems."""
def __init__(self, llm_judge, medical_knowledge_base=None):
super().__init__("medical_rag_evaluator", llm_judge)
self.medical_knowledge_base = medical_knowledge_base or {}
# Medical-specific evaluation criteria
self.medical_criteria = {
'clinical_accuracy': 0.40, # Clinical accuracy and safety
'evidence_quality': 0.25, # Quality of cited evidence
'contraindication_awareness': 0.20, # Awareness of risks/contraindications
'professional_tone': 0.10, # Appropriate professional language
'disclaimer_presence': 0.05 # Medical disclaimers present
}
# Medical terminology and patterns
self.medical_patterns = {
'drug_names': r'[A-Z][a-z]+(?:ine|ol|ide|ium|ate)\b',
'dosages': r'\d+\s*(?:mg|mcg|g|ml|cc)\b',
'conditions': r'(?:syndrome|disease|disorder|condition)\b',
'procedures': r'(?:surgery|procedure|therapy|treatment)\b'
}
# Safety-critical terms that require careful handling
self.safety_terms = [
'contraindicated', 'adverse', 'side effect', 'warning',
'caution', 'risk', 'dangerous', 'toxic', 'lethal'
]
def evaluate(self, query: str, response: str, contexts: List[str],
**kwargs) -> CustomEvaluationResult:
"""Specialized medical RAG evaluation."""
# Clinical accuracy assessment
accuracy_score = self._assess_clinical_accuracy(response, contexts)
# Evidence quality evaluation
evidence_score = self._evaluate_medical_evidence(response, contexts)
# Contraindication and safety assessment
safety_score = self._assess_safety_awareness(response)
# Professional tone evaluation
tone_score = self._evaluate_professional_tone(response)
# Disclaimer presence check
disclaimer_score = self._check_medical_disclaimers(response)
# Composite medical scoring
component_scores = {
'clinical_accuracy': accuracy_score,
'evidence_quality': evidence_score,
'contraindication_awareness': safety_score,
'professional_tone': tone_score,
'disclaimer_presence': disclaimer_score
}
overall_score = sum(
component_scores[criterion] * weight
for criterion, weight in self.medical_criteria.items()
)
return CustomEvaluationResult(
metric_name=self.name,
score=overall_score,
confidence=self._calculate_medical_confidence(component_scores),
explanation=self._generate_medical_explanation(component_scores),
component_scores=component_scores,
improvement_suggestions=self._generate_medical_improvements(component_scores),
metadata={'safety_flags': self._identify_safety_flags(response)}
)
This medical evaluator ensures healthcare RAG systems meet the critical safety and accuracy standards required in medical applications.
⚙️ Advanced LLM-as-a-Judge Systems¶
Multi-Perspective Evaluation Framework¶
Let's implement a sophisticated LLM-as-a-Judge system that uses multiple evaluation perspectives for robust assessment:
class MultiPerspectiveLLMJudge:
"""Advanced LLM evaluation using multiple judge perspectives."""
def __init__(self, llm_models, perspective_configs):
self.llm_models = llm_models # Multiple LLM models for diversity
self.perspective_configs = perspective_configs
self.evaluation_history = []
def evaluate_from_multiple_perspectives(self, query: str, response: str,
contexts: List[str]) -> Dict[str, Any]:
"""Evaluate response from multiple expert perspectives."""
perspective_results = {}
for perspective_name, config in self.perspective_configs.items():
print(f"Evaluating from {perspective_name} perspective...")
# Select appropriate LLM for this perspective
llm_model = self._select_model_for_perspective(perspective_name)
# Generate perspective-specific evaluation
perspective_result = self._evaluate_single_perspective(
query, response, contexts, perspective_name, config, llm_model
)
perspective_results[perspective_name] = perspective_result
# Aggregate perspectives into final assessment
final_assessment = self._aggregate_perspective_results(perspective_results)
# Store evaluation history
self.evaluation_history.append({
'query': query,
'response': response,
'perspective_results': perspective_results,
'final_assessment': final_assessment,
'timestamp': time.time()
})
return final_assessment
This multi-perspective approach reduces bias and provides more robust evaluation by considering different expert viewpoints.
Advanced Evaluation Prompt Engineering¶
Let's implement sophisticated prompt engineering for reliable LLM-as-a-Judge evaluation:
def _create_advanced_evaluation_prompt(self, query: str, response: str,
contexts: List[str], perspective: str,
config: Dict) -> str:
"""Create sophisticated evaluation prompt with multiple quality dimensions."""
context_text = '\n\n'.join([f"Context {i+1}: {ctx}" for i, ctx in enumerate(contexts[:3])])
evaluation_prompt = f"""You are an expert {perspective} evaluator assessing AI-generated responses.
EVALUATION TASK: Assess the quality of this response across multiple dimensions.
QUERY: {query}
RESPONSE: {response}
AVAILABLE CONTEXTS:
{context_text}
EVALUATION FRAMEWORK:
Please evaluate the response on each dimension using the 1-5 scale:
5 - Excellent: Exceeds expectations across all criteria
4 - Good: Meets expectations with minor areas for improvement
3 - Satisfactory: Adequate performance with some notable gaps
2 - Poor: Below expectations with significant issues
1 - Very Poor: Fails to meet basic requirements
DIMENSIONS TO EVALUATE:
"""
# Add dimension-specific criteria
for dimension, criteria in config['dimensions'].items():
evaluation_prompt += f"""
{dimension.upper()}:
{criteria['description']}
Specific criteria: {', '.join(criteria['criteria'])}
"""
evaluation_prompt += f"""
RESPONSE FORMAT:
For each dimension, provide:
- SCORE: [1-5]
- EVIDENCE: [Specific examples from the response supporting your score]
- IMPROVEMENT: [Specific suggestions for improvement]
Then provide:
- OVERALL_SCORE: [Average of dimension scores]
- SUMMARY: [2-3 sentence overall assessment]
- KEY_STRENGTHS: [Main strengths of the response]
- KEY_WEAKNESSES: [Main areas needing improvement]
Begin your evaluation:
"""
return evaluation_prompt
This advanced prompt engineering ensures comprehensive, structured evaluation that provides actionable feedback.
Practice Implementation Exercises¶
Exercise 1: Domain-Specific Evaluator¶
- Choose a specialized domain (e.g., financial, technical, educational)
- Define 5 domain-specific evaluation criteria with weights
- Implement complete custom evaluator class
- Test on domain-specific RAG outputs and document results
Exercise 2: Multi-Dimensional Retrieval Assessment¶
- Implement comprehensive retrieval evaluator with 4+ dimensions
- Include semantic, coverage, diversity, and quality assessments
- Test against different retrieval strategies
- Create performance comparison dashboard
Exercise 3: Advanced LLM-as-a-Judge System¶
- Design 3 different expert perspectives for evaluation
- Implement multi-perspective evaluation framework
- Compare single vs. multi-perspective evaluation reliability
- Document bias reduction and accuracy improvements
Integration and Deployment Patterns¶
Custom Metrics Integration¶
class CustomMetricsEvaluationPipeline:
"""Integration pipeline for custom evaluation metrics."""
def __init__(self):
self.registered_metrics = {}
self.evaluation_configs = {}
def register_custom_metric(self, metric_name: str, metric_instance):
"""Register custom evaluation metric."""
self.registered_metrics[metric_name] = metric_instance
def create_evaluation_config(self, config_name: str, metrics_config: Dict):
"""Create evaluation configuration using custom metrics."""
self.evaluation_configs[config_name] = metrics_config
def run_custom_evaluation_suite(self, evaluation_data: List[Dict],
config_name: str) -> Dict[str, Any]:
"""Run comprehensive evaluation using custom metrics."""
if config_name not in self.evaluation_configs:
raise ValueError(f"Configuration '{config_name}' not found")
config = self.evaluation_configs[config_name]
results = {
'config_name': config_name,
'metric_results': {},
'aggregate_scores': {},
'recommendations': []
}
# Run each configured metric
for metric_name, metric_config in config.items():
if metric_name in self.registered_metrics:
metric_instance = self.registered_metrics[metric_name]
# Run metric evaluation
metric_results = metric_instance.batch_evaluate(evaluation_data)
results['metric_results'][metric_name] = metric_results
# Calculate aggregate scores
scores = [r.score for r in metric_results if r.score is not None]
if scores:
results['aggregate_scores'][metric_name] = {
'mean': np.mean(scores),
'std': np.std(scores),
'min': min(scores),
'max': max(scores)
}
return results
This integration pattern enables seamless deployment of custom metrics in production evaluation pipelines.
Learning Path Summary¶
⚙️ Implementer Path Mastery: You've built sophisticated custom evaluation metrics, domain-specific evaluators, and advanced LLM-as-a-Judge systems. You can now create specialized evaluation frameworks that capture the unique quality requirements of your RAG applications.
Advanced Capabilities Achieved:
- Domain-specific evaluation metric design and implementation
- Multi-dimensional retrieval quality assessment
- Specialized evaluators for legal, medical, and other critical domains
- Advanced LLM-as-a-Judge systems with multiple perspectives
- Custom metric integration and deployment patterns
Next Advanced Topic:
- ⚙️ Implementer Path: Enterprise Monitoring Systems → - Production-scale monitoring and alerting infrastructure
🧭 Navigation¶
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