LLM POC Evaluation: Test Set, Success Criteria, Failure Modes

Building a chat box is easy; building an LLM system that delivers business-accurate results every time is an engineering challenge. AI is non-deterministic. To prove feasibility, you need a "Ground Truth" test set and a rigorous failure mode analysis. The glossary definition of what a POC is helps you frame this evaluation correctly — an LLM POC is proving technical feasibility, not product value, and that distinction changes what you measure.

TL;DR

• Ground Truth: 50-100 real-world examples with the "Ideal Answer."
• Failure Modes: Specifically identifying where the AI "hallucinates" or refuses to answer.
• Evaluation: Using GPT-4 or Claude as a "judge" to score your POC's output.
• Cost/Latency: Measuring the real price of accuracy.

The Evaluation Workflow

1. Data Collection (Ground Truth)

A POC must be tested against a static set of inputs. If you change the input every time, you can't measure progress. We work with you to define 50 "Edge Cases" that the AI must handle.

2. Failure Mode Analysis

We don't just look at "Success Rate." We categorize failures:

• Hallucination: The AI made up a fact.
• Constraint Violation: The AI ignored a business rule (e.g., "Don't mention competitors").
• Latency Timeout: The result took >15 seconds.

3. The "Judge" Architecture

We use a high-end model (like Claude 3.5 Sonnet) to evaluate the output of the POC's primary model. This provides a quantitative score (0-10) for every response.

Why "Prompt Engineering" isn’t a POC

A POC at HouseofMVP’s is about Logic and Data Boundaries. We build the specialized wrappers, RBAC controls, and retry logic that make the model production-ready. Our AI agent development service transitions proven LLM POCs into production systems with proper security boundaries and evaluation pipelines. The MVP Cost Calculator helps you budget the full build after the POC establishes what the production system needs. For a structured look at how to turn these results into a build/no-build decision, see how to build a POC properly.

Common Mistakes

• Testing with 5 Questions: Assuming it works just because it answered you correctly twice.
• Ignoring the Prompt "Leak": Not testing if users can trick the AI into giving up system secrets.
• No Persistence: Not testing how the AI handles long chat histories or context window limits.

FAQ

Can you prove RAG accuracy? Yes, we measure "Faithfulness" (Is it based on the data?) and "Relevance" (Did it answer the question?).

How many models do you test? We typically benchmark across 3 models (GPT, Claude, Gemini) during a 7-day POC.

What about cost? Every POC includes a "Cost-per-Query" projection for your business model.

is the code secure? Yes. We implement strict Data Boundaries.

Do I need my own API keys? We can use yours or provide a sandbox environment.

What is the outcome? A "Go/No-Go" decision based on measurable feasibility.

Next Steps

Bring data science to your AI vision. Explore our AI Services or start a POC.

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