Latest insights on AI, DX, and global business
Learn how fine-tuning works from the basics. Covers PEFT/LoRA, when to use RAG, cost estimates, and a checklist for B2B companies evaluating custom model development.
AI grounding anchors LLM responses to authoritative sources. Learn how RAG and web search reduce hallucinations, plus implementation steps for enterprise deployment.
AI Gateway is a relay layer managing multiple LLM providers via a unified interface. A design and operations guide for implementing cost optimization, failover, and audit logging.
Synthetic Test evaluates AI using synthetic data. We explain its role in LLM & AI agent quality assurance, differences from LLM-as-a-Judge, and 4 implementation steps.
Learn how Test-Time Compute scaling works and how to optimize the tradeoff between inference cost and accuracy. A practical guide to LLM operations and cost design in the age of reasoning models.
Explore hybrid AI design combining cloud LLM and on-device SLM, with strategies for routing tasks by cost, latency, and compliance.
Protect LLM apps from prompt injection & hallucinations. Learn guardrail design basics, input/output guards, evaluation, and multi-tenant operations from an implementation perspective.
Learn the basics of edge AI & on-device LLMs, and how to design workflows requiring low latency, data privacy, or unstable connectivity—where cloud LLMs fall short.
Context Engineering is a design approach for dynamically assembling optimal information for LLM apps. Covers differences from prompt engineering, context window components, and RAG/agent implementation patterns.
LLM-as-a-Judge automates LLM output scoring. Covers Pointwise/Pairwise/Reference, bias mitigation, 4 implementation steps, and how it fits with Observability and Guardrails.
Practical guide to cutting LLM production costs. Learn 4 pillars—token optimization, model selection, prompt caching & RAG design—to halve monthly costs without sacrificing accuracy.
From AI observability basics to tracing, evaluation, and cost management for production LLM apps—learn key implementation steps and tool selection tips.
