The Trust Gap in GenAI | Gabriel Ordonez

Why Your CEO Doesn't Care About Perplexity

In early 2024, I sat in a boardroom where an engineering lead pitched a new Llama-3 based chatbot. They showed impressive Hugging Face leaderboard scores. The CMO asked one question:

"Can you guarantee it won't recommend our competitor?"

Silence. That moment crystallized the trust gap between academic AI metrics and business requirements.

The Problem with Academic Benchmarks

MMLU scores don't tell you if your chatbot will maintain brand voice. Perplexity doesn't measure whether responses are safe for customer-facing deployment. Business stakeholders need different metrics.

Designing Business-First Metrics

To cross the chasm from "cool demo" to "production tool," we need frameworks that measure what actually matters. Enter RAGAs (Retrieval Augmented Generation Assessment)—the emerging gold standard for production evaluation.

Metric	What It Measures	Business Impact
Faithfulness	Does the answer match retrieved context?	Reduces hallucination risk
Answer Relevancy	Is the response on-topic?	Customer satisfaction
Context Precision	Are relevant chunks ranked higher?	Retrieval efficiency
Brand Safety	Does output align with guidelines?	Legal/reputation risk

The "LLM-as-a-Judge" Paradigm

We can't rely on human evaluation at scale. The solution: use a stronger model (GPT-4 or Claude 3 Opus) to evaluate outputs of smaller, faster models. This enables automated regression testing on "tone," "safety," and "brand alignment" with every commit.

LLM-as-Judge Implementation

class LLMJudge:
    """Use a stronger model to evaluate weaker model outputs"""

    def __init__(self, judge_model="claude-3-opus"):
        self.judge = AnthropicClient(model=judge_model)
        self.rubric = self._load_evaluation_rubric()

    def evaluate(self, query: str, response: str, context: str):
        prompt = f"""
        Evaluate this response on a scale of 1-5 for:
        1. Faithfulness to context
        2. Answer relevancy
        3. Brand safety

        Query: {query}
        Context: {context}
        Response: {response}

        Return JSON with scores and reasoning.
        """

        return self.judge.complete(prompt)

Cost Optimization via Evaluation

Rigorous eval frameworks also unlock cost savings. By benchmarking prompt performance, I was able to prove that for 80% of queries, a smaller, cheaper model performed identically to the flagship model.

80% Queries

Can use smaller model

25% Cost Saved

Via intelligent routing

0% Quality Loss

On routed queries

The key insight: you can't optimize what you can't measure. Build the evaluation framework first, then use it to justify model selection decisions to stakeholders.

Building Your Evaluation Pipeline

1. Create a Golden Dataset

Curate 100-500 representative query-response pairs with human-labeled quality scores.

2. Define Business Metrics

Work with stakeholders to define what "good" means: brand voice, safety boundaries, accuracy thresholds.

3. Implement LLM-as-Judge

Use a strong evaluator model with detailed rubrics to score outputs automatically.

4. Integrate into CI/CD

Run evaluations on every PR. Block merges if quality scores drop below thresholds.

Key Takeaways

Academic benchmarks (MMLU, perplexity) don't address business concerns
RAGAs framework measures faithfulness, relevancy, and precision
LLM-as-Judge enables automated quality testing at scale
Evaluation frameworks unlock cost optimization opportunities
Build eval pipelines before deploying to production

The Trust Gap in
GenAI