Explore guides, research notes, tutorials, and perspectives from the AI community.
AI ecosystem
A practical framework for building and maintaining LLM evaluation datasets that provide trustworthy, actionable insights through clear criteria, high-quality datasets, versioning strategies, and automated validation.
AI ecosystem
Build a reliable code generation pipeline using an iterative loop of generation, sandboxed execution, and feedback-driven refinement to eliminate bugs and improve correctness.
Agent Design
Plan an AI feature retirement with explicit transition states, historical evidence, outstanding-job handling and a clear replacement experience.
AI ecosystem
A practical guide to instrumenting LLM applications with structured logging, distributed tracing, and debuggable pipelines so you can diagnose failures, understand latency, and improve quality in production.
AI ecosystem
Comprehensive guide to designing scalable multi-agent LLM systems with supervisor-worker hierarchies, swarm patterns, and pipeline architectures for complex problem-solving workflows.
AI ecosystem
A practical guide to defending LLM applications against prompt injection using system prompt hardening, input filtering, output validation, external content segregation, least privilege, human approval, and adversarial testing.
AI ecosystem
Design production-ready LLM function calling pipelines with input validation, retry strategies, error categorization and structured output for reliable tool use in applications.
AI ecosystem
Build a reliable LLM-as-a-judge evaluation pipeline using a scoring rubric, golden set, evaluator-optimizer loop, and Cohen's kappa agreement checks.
AI Evaluation
A practical pipeline that separates AI preparation from human decision-making in code reviews.
AI ecosystem
Control LLM API usage with job budgets, bounded retries, traceable usage and careful caching. Evaluate savings against the same quality requirements.
AI ecosystem
Compare fine-tuning and RAG through failure diagnosis, controlled experiments, operating costs and evaluation, without assuming either approach always wins.
AI ecosystem
Evaluate a RAG system with retrieval checks, faithfulness, answer relevance and a repeatable workflow that separates retrieval and generation failures.