Author
Kenny Tan
Co-founder & Technical Lead
AI Model Evaluation Systems · LLM Benchmark Infrastructure · Prompt Engineering Research
Builds the evaluation infrastructure behind every AI assessment we publish.
Cross-domain expertise in software engineering, content systems, and infrastructure architecture.
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Written by Kenny Tan
- AI Agent Design Patterns — Tool Use, Planning, and Memory Architectures 15 Apr 2026
- AI API Integration Patterns — Direct Call vs Streaming vs Batch Processing 15 Apr 2026
- AI Bias Detection — Demographic Parity, Equal Opportunity, Calibration, and When Each Metric Applies 15 Apr 2026
- AI Content Filtering — Guardrails That Block Without Breaking User Experience 15 Apr 2026
- AI Cost Optimization in Production — Techniques That Cut Spend by 60-80% 15 Apr 2026
- AI Evaluation Frameworks — Test Suites That Catch Regressions Before Users Do 15 Apr 2026
- AI Feature Flagging — Gradual Rollout, A/B Testing, and Safe Deployment Patterns 15 Apr 2026
- Types of AI Hallucinations — Factual, Logical, Attribution, and How to Detect Each 15 Apr 2026
- AI Model Audit Guide — Pre-Deployment Testing for EU AI Act, NIST, and ISO 42001 15 Apr 2026
- AI Model Latency Comparison — TTFT, Throughput, and Real-Time Performance Data 15 Apr 2026
- AI Observability in Production — What to Measure, When to Alert, and What to Ignore 15 Apr 2026
- AI Transparency and Explainability — SHAP, LIME, Attention, and When Each Method Works 15 Apr 2026
- Embedding Models Compared — Dimensions, Speed, Cost, and Retrieval Quality 15 Apr 2026
- Fine-Tuning vs Prompt Engineering — The Decision Framework with Cost Breakpoints 15 Apr 2026
- Hallucination Detection Methods — RAG Faithfulness, Semantic Similarity, and Production Pipelines 15 Apr 2026
- LLM Safety Testing — Red Teaming, Adversarial Prompts, and Systematic Attack Taxonomies 15 Apr 2026
- Local vs Cloud AI Deployment — Cost Breakpoint Analysis for On-Device vs API 15 Apr 2026
- Model Evaluation Beyond Benchmarks — Why MMLU Doesn't Predict Production Performance 15 Apr 2026
- Multi-Turn Conversation Design — Context Management, Memory Patterns, and Reset Strategies 15 Apr 2026
- Multimodal Model Comparison — Vision, Audio, and Document Understanding Across GPT-4o, Claude, and Gemini 15 Apr 2026
- Open vs Closed AI Models — Llama, Mistral, GPT-4, Claude Decision Framework 15 Apr 2026
- Prompt Injection Defense — Attack Classification, Sanitization Patterns, and Defense Effectiveness Rates 15 Apr 2026
- Prompt Testing Methodology — A/B Evaluation, Test Suites, and Regression Detection 15 Apr 2026
- RAG Architecture — Prototype to Production in Three Stages 15 Apr 2026
- Responsible AI Deployment Checklist — 40 Points from Prototype to Production 15 Apr 2026
- Vector Database Comparison — Pinecone, Weaviate, Chroma, Qdrant, and 4 More 15 Apr 2026
- AI Model Benchmarks That Actually Matter — Beyond MMLU and HumanEval 13 Apr 2026
- AI Model Pricing Decoded — Cost Per Million Tokens Across GPT-4o, Claude, Gemini, and Llama 13 Apr 2026
- Chain-of-Thought vs. Direct Prompting — When Reasoning Steps Actually Help 13 Apr 2026
- Choosing the Right AI Model for Your Task — A Decision Framework 13 Apr 2026
- Context Window Comparison — What 128K, 200K, and 1M Tokens Actually Means for Your Workflow 13 Apr 2026
- Output Formatting Control — JSON, Markdown, CSV, and Structured Responses 13 Apr 2026
- How to Cut AI API Costs by 60-80% Without Losing Quality 13 Apr 2026
- Temperature and Top-P Explained — How Sampling Parameters Change Your Output 13 Apr 2026
- Token Optimization — How to Get the Same Output Quality at 40% Lower Cost 13 Apr 2026
- System Prompt Patterns That Actually Work 12 Apr 2026