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What You’ll Learn

This interactive notebook demonstrates advanced evaluation patterns for production LLM applications through comprehensive testing with the TruthfulQA benchmark dataset. Key Topics Covered:
  • Advanced data import with CSV/JSONL and complex column mapping
  • Real LLM integration with production-ready task functions
  • Context-aware evaluators for RAG and knowledge-grounded applications
  • Multi-score evaluators and advanced evaluation patterns
  • Complex parameter mapping with lambda functions
  • Production experiments with 11+ evaluators and complete analysis

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Interactive Tutorial

The notebook guides you through building a comprehensive experiment pipeline for any LLM application, from single-turn Q&A to multi-turn conversations, RAG systems, and agentic workflows. Open the Advanced Evaluations Notebook in Google Colab → Or download the notebook directly from GitHub →

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Prerequisites

  • Fiddler account with API credentials
  • Basic familiarity with the Evals SDK Quick Start
  • Optional: OpenAI API key for real LLM examples (mock responses available)

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Time Required

  • Complete tutorial: 45-60 minutes
  • Quick overview: 15-20 minutes

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Tutorial Highlights

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Key Takeaways from the Advanced Tutorial

Even if you prefer to run the notebook, here are the critical patterns you’ll learn:

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1. Complex Data Import Strategies

CSV Import with Column Mapping: 
dataset.insert_from_csv_file(
    file_path='truthfulqa.csv',
    input_columns=['question', 'category'],
    expected_output_columns=['best_answer'],
    extras_columns=['context'],  # Separate context for RAG evaluation
    metadata_columns=['type', 'difficulty']
)
Why This Matters: Production datasets rarely have perfect column names. Column mapping lets you use any data source without reformatting files.

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2. Context-Aware Evaluation for RAG Systems

Faithfulness Checking: Fiddler provides two faithfulness evaluators: RAGFaithfulness (LLM-as-a-Judge, part of the RAG Health Metrics triad) for comprehensive diagnostics, and FTLResponseFaithfulness (Centor Faithfulness model) for low-latency guardrails. 
from fiddler_evals.evaluators import RAGFaithfulness, FTLResponseFaithfulness

# RAG Faithfulness — LLM-as-a-Judge for RAG pipeline diagnostics
rag_faithfulness = RAGFaithfulness(model="openai/gpt-4o", credential="your-llm-credential")
# Requires user_query, rag_response, and retrieved_documents

# Centor Faithfulness — Fiddler Centor Model for low-latency guardrails
ftl_faithfulness = FTLResponseFaithfulness()
# Requires context and response only
Why This Matters: RAG systems must be evaluated differently than simple Q&A. Use RAGFaithfulness with the full RAG Health Metrics triad (Answer Relevance, Context Relevance) for root cause diagnosis. Use FTLResponseFaithfulness for real-time guardrails where latency matters.

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3. Multi-Score Evaluators

Sentiment with Probability Scores: 
from fiddler_evals.evaluators import Sentiment

# Returns multiple scores: sentiment (categorical) and probability (float)
sentiment = Sentiment()  # Fiddler Centor Model — no model/credential needed

# Returns multiple scores: sentiment label and probability
# - score.label: "positive" | "neutral" | "negative"
# - score.value: confidence score 0.0-1.0
Why This Matters: Some quality dimensions have multiple facets. Multi-score evaluators capture nuanced assessments in a single pass.

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4. Production Experiment Patterns

Multiple Evaluators in One Experiment: 
evaluators = [
    # RAG Health Metrics (diagnostic triad)
    AnswerRelevance(model="openai/gpt-4o", credential="your-llm-credential"),
    ContextRelevance(model="openai/gpt-4o", credential="your-llm-credential"),
    RAGFaithfulness(model="openai/gpt-4o", credential="your-llm-credential"),

    # Quality metrics
    Coherence(model="openai/gpt-4o", credential="your-llm-credential"),
    Conciseness(model="openai/gpt-4o", credential="your-llm-credential"),

    # Safety metrics
    FTLPromptSafety(),
    FTLResponseFaithfulness(),

    # Domain-specific (Centor Model — no model/credential needed)
    Sentiment(),
    RegexSearch(pattern=r'\b[A-Z][a-z]+\s[A-Z][a-z]+\b'),  # Proper nouns
    CustomDomainEvaluator(),
]

results = evaluate(
    dataset=large_dataset,
    task=production_task,
    evaluators=evaluators,
    max_workers=10,  # Parallel processing
    metadata={"version": "v2.1", "environment": "staging"}
)
Why This Matters: Production systems need comprehensive evaluation across multiple dimensions. This pattern shows how to run extensive experiment suites efficiently.

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5. Advanced Parameter Mapping

Complex Data Structures: 
score_fn_kwargs_mapping={
    # Simple mappings
    "response": "answer",

    # Extract from nested dicts
    "prompt": lambda x: x["inputs"]["question"],
    "context": lambda x: x["extras"]["retrieved_docs"],

    # Compute values
    "output_length": lambda x: len(x["outputs"]["answer"]),

    # Combine values
    "full_conversation": lambda x: x["inputs"]["history"] + [x["outputs"]["answer"]],
}
Why This Matters: Real applications have complex data structures. Lambda-based mapping gives you the flexibility to extract any value an evaluator needs.

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Advanced Data Import

Learn how to import complex experiment datasets with:
  • CSV and JSONL file support with column mapping
  • Separation of inputs, extras, expected outputs, and metadata
  • Source tracking for test case provenance
  • Support for RAG context and conversation history

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Production Evaluator Suite

Build a comprehensive evaluation with:
  • Context-aware evaluators: Faithfulness checking for RAG systems
  • Safety evaluators: Prompt safety and faithfulness detection
  • Quality evaluators: Relevance, coherence, and conciseness
  • Custom evaluators: Domain-specific metrics for complete customization
  • Multi-score evaluators: Sentiment and topic classification

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Complex Parameter Mapping

Master advanced mapping techniques:
  • Lambda-based parameter transformation
  • Access to inputs, extras, outputs, and metadata
  • Flexible mapping for any evaluator signature
  • Production-ready patterns for all LLM use cases

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Comprehensive Analysis

Extract insights from experiment results:
  • Aggregate statistics by evaluator
  • Performance breakdown by category
  • DataFrame export for further analysis
  • A/B testing and regression detection patterns

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Who Should Use This

  • AI engineers building production LLM applications
  • ML engineers implementing systematic experiment pipelines
  • Data scientists analyzing LLM performance and quality
  • QA engineers setting up regression testing for AI systems

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Use Case Flexibility

The patterns demonstrated work for all LLM application types:
  • Single-turn Q&A: Direct question-answering without context
  • RAG applications: Context-grounded responses with faithfulness checking
  • Multi-turn conversations: Dialogue systems with conversation history
  • Agentic workflows: Tool-using agents with intermediate outputs
  • Multi-task models: Systems handling diverse request types

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Centor Models Integration

All evaluators in the advanced tutorial run on Fiddler Centor Models, which means:

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Cost Efficiency at Scale

Running multiple evaluators on 817 test cases (TruthfulQA dataset) would typically cost:
  • External LLM API: $50-100+ in API calls (0.01¢ per evaluation × 9,000 evaluations)
  • Fiddler Centor Models: $0 (no per-request charges)

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Performance at Scale

  • Parallel execution: 10 workers process 817 items in ~5 minutes
  • Fast evaluators: <100ms per evaluation enables real-time feedback
  • No rate limits: No API quota concerns for extensive batch experiments

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Security

  • Data locality: All evaluations run within your Fiddler environment
  • No external calls: Your prompts and responses never leave your infrastructure
  • Audit trail: Complete traceability for compliance
This makes Fiddler Experiments ideal for enterprise-scale experiment pipelines.

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Next Steps

After completing the tutorial: