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Why Evaluate LLM Applications?

Systematic evaluation of AI-powered applications is critical for ensuring quality, reliability, and continuous improvement.

The Challenge

Unlike traditional software, Large Language Model (LLM) applications produce non-deterministic outputs. The same query can produce different responses, making traditional testing approaches insufficient.

Traditional testing:

  • Unit tests verify specific outputs
  • Integration tests check exact API responses
  • Pass/fail is binary

LLM applications:

  • Responses vary even with identical inputs
  • Multiple correct answers exist for one question
  • Quality exists on a spectrum

Why Evaluation Matters

1. Quality Assurance

Ensure the chatbot provides accurate, relevant answers to business questions without relying on manual spot-checking.

2. Regression Detection

Detect when code changes or data updates degrade response quality before deploying to production.

3. Continuous Improvement

Track improvements over time as you enhance prompts, tools, or data sources.

4. Tool Usage Validation

Verify the AI correctly chooses database queries vs. document search for different question types.

Poolula Platform Evaluation Approach

Three-Tier Strategy

graph TD
    A[Evaluation Strategy] --> B[1. Traditional Testing]
    A --> C[2. Golden Question Set]
    A --> D[3. Production Monitoring]

    B --> B1[pytest unit tests]
    B --> B2[API integration tests]
    B --> B3[Code coverage ≥80%]

    C --> C1[15 representative questions]
    C --> C2[Expected tool usage]
    C --> C3[Keyword scoring]

    D --> D1[Audit logging]
    D --> D2[Response time tracking]
    D --> D3[Error monitoring]

    style C fill:#e1f5fe
    style C1 fill:#b3e5fc
    style C2 fill:#b3e5fc
    style C3 fill:#b3e5fc

Golden Question Set

A curated set of 15 questions representing core use cases:

  • Property information queries
  • Financial calculations
  • Transaction searches
  • Document searches
  • Hybrid queries (database + documents)

Each question includes:

  • Expected tools the AI should use
  • Keywords the response should contain
  • Category for performance tracking

Automated Scoring

Responses are scored on three components:

  1. Tool Usage (40%) - Did the AI select the correct tools?
  2. Response Quality (40%) - Does the answer contain expected information?
  3. Error Handling (20%) - No crashes or error responses

This produces a 0-100% score for each question and overall performance metrics.

Learn More

Key Insights

For Portfolio/Employers:

This evaluation framework demonstrates:

  • Understanding of AI-specific quality challenges
  • Systematic approach to testing non-deterministic systems
  • Production-ready thinking (monitoring, baselines, iteration)
  • Ability to balance multiple quality dimensions

For Development:

The evaluation harness provides:

  • Fast feedback during development
  • Objective quality metrics
  • Regression prevention
  • Data-driven improvement decisions