Designed and implemented a structured evaluation framework to quantify LLM output quality across multiple dimensions, focusing on hallucination detection, logical consistency, and instruction adherence in production workflows.
Key Features
- Multi-dimensional scoring pipeline:
- Hallucination detection (factual grounding vs unsupported claims)
- Logical consistency (internal coherence and contradiction checks)
- Instruction adherence (prompt compliance scoring)
- Response completeness (coverage of required outputs)
- Weighted scoring system for composite quality index
- Standardized evaluation schema for reproducibility across test sets
- Batch evaluation pipeline for large-scale prompt testing
- Support for A/B testing across prompt variants and model outputs
System Design
- Input layer:
- Prompt + model response pairs
- Ground truth / reference signals (where applicable)
- Processing layer:
- Text normalization and token-level analysis
- Rule-based and heuristic scoring functions
- Metric aggregation using weighted scoring
- Storage layer:
- Structured evaluation logs (CSV / DataFrame)
- Historical tracking for regression analysis
How It Works
- Convert raw outputs into evaluation-ready format
- Apply metric-specific scoring functions:
- Hallucination → mismatch with known context / unsupported claims
- Consistency → contradiction detection across response segments
- Adherence → prompt constraint matching
- Compute weighted final score:
- Final Score = Σ (metric_score × weight)
- Store outputs for:
- prompt iteration
- model comparison
- failure case analysis
Why It Matters
- Enables quantitative evaluation of LLM outputs instead of subjective review
- Detects hallucination patterns in production systems
- Supports prompt optimization using measurable signals
- Reduces degradation across model updates and prompt changes
Tech Stack
- Python
- Pandas
- NumPy
- Prompt Engineering
- Evaluation Pipeline Design
Output
- Structured scoring datasets
- Prompt-wise performance comparison
- Regression tracking across evaluation runs