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A pragmatic framework for testing LLM-powered features: how to design automated and semi-automated pipelines, run CI-friendly evaluations, build regression suites, and monitor quality over time.
A practical playbook for small engineering teams to forecast the cost of AI coding assistants, compare subscription vs API models, and build a defendable ROI model.
A focused, practical comparison of Kiro, Codex, and GitHub Copilot for small engineering teams. Covers integration, customization, privacy, cost models, and actionable checklists to choose the right assistant.
Practical patterns for running multiple LLMs in production: routing rules, fallback strategies, ensemble patterns, and how to balance cost vs latency to meet SLOs.
Concrete practices for teams to version, test, and govern prompts so changes are trackable, reversible, and safe in production.
A practical, ordered checklist to build and maintain a production-ready retrieval augmented generation pipeline—from ingestion and chunking to retrieval tuning, answer verification, and operations.
How to design and implement an LLM testing framework and regression suite that detects hallucinations, behavioral regressions, and performance drift—practical test types, metrics, CI practices, and triage workflows.
Practical playbook for llm cost forecasting: formulas, monitoring metrics, alert types, and optimization steps product and engineering teams can apply to estimate and control LLM spend.
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