AI Engineering
Evaluation, observability, guardrails, security, and production operations for AI systems.
- AI Evaluation
Hub guide to evaluating AI systems — four eval layers, golden sets, LLM-as-judge, CI gates, and why public benchmarks are not product readiness.
- LLM Evaluation
Measuring LLM output quality — automated checks, rubrics, LLM-as-judge, human calibration, and CI eval pipelines for generation.
- RAG Evaluation
Evaluating RAG systems — faithfulness, answer relevance, context precision/recall, and attributing failures to retrieval vs generation.
- Prompt Evaluation
Systematically testing prompts — versioning, A/B comparison, format compliance, and regression detection in CI.
- Benchmarks
Public LLM and embedding benchmarks — what they measure, contamination/gaming risks, and how to shortlist models without mistaking leaderboards for product readiness.
- AI Observability
Production observability for LLM systems — traces with prompt/completion/tool spans, cost and latency attribution, quality proxies, and PII-safe instrumentation.
- AI Security
Threat modeling for LLM apps — prompt injection, tool abuse, data exfiltration, and defenses with guardrails, privilege separation, and red teaming.
- Cost Optimization
Reducing LLM spend — workload-based model routing, prompt compression, caching, batching, and token-aware context design.
- Latency Optimization
Making AI systems fast — TTFT, streaming, parallel retrieval, caching, and routing simple steps to faster model tiers.
- Caching
Caching for LLM apps — exact response cache, embedding cache, provider prompt/prefix cache, TTL, and stampede control.
- Semantic Caching
Similarity-based response cache — embed queries, return prior answers above a threshold, and manage false-positive risk.