Evaluation & Quality Assurance
GenAI Evaluation &
Trust Architecture
Trust is engineering, not magic. We implement rigorous Automated Metrics, Human-in-the-Loop workflows, and Continuous Regression Testing to ensure your AI models are accurate, safe, and reliable.
Assessment Frameworks
Explainable Metrics
Scores like "Accuracy: 80%" are opaque. We implement RAGAS (Retrieval Augmented Generation Assessment) to provide explainable metrics. We decompose answers into claims and verify each against the source text to pinpoint exactly where hallucinations occur.
Metric Inspector: Faithfulness
Score: 0.65
Source Context (Ground Truth)
"The Apollo 11 mission launched on July 16, 1969. Neil Armstrong and Buzz Aldrin walked on the moon."
Generated Answer Analysis
The Apollo 11 mission launched in 1969.Neil ArmstrongandMichael Collinswalked on the moon.
Failure Detected: The model hallucinated details not present in the source text or failed to address the user intent directly.
LLM-as-a-Judge
AI Critiquing AI
Human evaluation doesn't scale. We implement LLM-as-a-Judge pipelines where a stronger "Teacher Model" (e.g., GPT-4) evaluates the outputs of your production model against strict rubrics. This allows for 24/7 quality assurance on subjective metrics like tone, empathy, and style.
Student Model Output (Llama-3-8B)
"Quantum computing is a type of computing that uses quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data. Unlike classical computers that use bits, quantum computers use qubits..."
Judge Analysis
Verdict
FAIL
Score
2/5
Reasoning Trace
The response uses jargon like 'quantum-mechanical phenomena' and 'superposition' which is too complex for a 5-year-old. It fails the target audience constraint.
Golden Dataset Benchmark
Comparing retrieval strategies against ground truth.
Query Inspection
"What is the standard SLA for Enterprise Gold tier?"
Top 3 Results (Hybrid + Rerank)
1. Gold Tier includes 24/7 support...
2. SLA Definition Document...
3. Support Escalation Policy...
Retrieval Testing
Search Diagnostics
A RAG system is only as good as its retriever. We assume nothing. We perform Context Relevance Testing using Golden Datasets (Question-Answer pairs). We measure Recall@K to ensure the correct document appears in the top results, optimizing chunk sizes and embedding models based on hard data.
Hit Rate Analysis
Monitoring how often the "correct" document is found in the top 5 results.
Lost-in-Middle Check
Verifying if the LLM ignores context placed in the middle of the prompt.
Agent Frameworks
Agent Debugger
Evaluating autonomous agents is complex because they are non-deterministic and multi-step. We implement Trajectory Analysis frameworks to track an agent's reasoning chain. We measure Task Completion Rate, Steps-to-Solution, and Self-Correction Capabilities (e.g., can it fix its own SQL syntax error?).
Key Metrics Tracked
Success Rate85%
Tool Error Rate12%
Avg Steps4.2
Execution Trace: #sess-8821Completed
THOUGHT
User wants to analyze Q3 sales. I need to find the database schema first.
ACTION
Tool: ListTables()
OBSERVATION
Tables: [users, orders, products]
THOUGHT
I will query the orders table.
ACTION
Tool: SQLQuery('SELECT * FROM orders WHERE date > 2023-13-01')
Error: Invalid Date Format
RECOVERY
Date format error. I need to fix the month.
ACTION
Tool: SQLQuery('SELECT * FROM orders WHERE date > 2023-12-01')
RLHF Labeling Workbench
Prompt: "Write a short poem about rust (the metal)."
Model A
"Iron turns to red, Decay spreads across the beam, Time eats all things whole."
Model B
"Rust is orange and bad. It makes cars look very old. I do not like it."
Human Feedback
RLHF & Preference Data
Automated metrics can't capture style or tone perfectly. We implement Human-in-the-Loop (HITL) workflows to collect preference data (A vs B). This data is used to train a Reward Model, which then aligns the LLM to your specific brand voice using Reinforcement Learning.
Feedback Mechanisms
- • Rank: User orders 3 responses best-to-worst.
- • Rewrite: Expert editor fixes the bad response.
- • Flag: User marks response as unsafe/incorrect.
QA Automation
Regression Testing
We treat prompts as code. Every change triggers an automated Evaluation Pipeline. We compare the new model outputs against a "Golden Dataset" to detect regressions in accuracy or tone before they reach production.
Commit: feat: update system prompt tone
Build
Unit Tests
RAGAS Eval
Production
Pipeline Failed: Quality Regression
Metric Answer Relevance dropped by 15% compared to baseline. Deployment blocked.