Blog
Practical posts on tracing, evals, prompt changes, token cost, and the production habits that keep AI products explainable.
Highlights from the Currai blog: the posts worth reading first.
Why AI agent evaluation still needs humans in 2026, where to put them in the loop, and how to combine human review with automated evals on production traces.
A practical field guide to LLM evaluation tools — what each category is good at, where they break down, and how to pick one that survives contact with production traffic.
The best AI observability tools in 2026 compared on evaluation depth, quality-aware alerting, drift detection, cost tracking, and the production-to-eval loop.
Browse implementation notes, observability guides, product decisions, and workflow ideas by topic.
Microsoft Copilot enterprise pricing now combines Microsoft 365 licenses, a $30 Copilot seat, Copilot Cowork usage, GitHub Copilot AI Credits, and optional agent capacity. This guide explains the real 2026 cost.
Read moreHow to run disciplined LLM experiments in 2026 — comparing prompts, models, and agent designs with evals so you ship changes that measurably improve quality.
Read moreThe common jailbreak techniques that get LLMs to break their own guardrails, why they work, and how to turn each one into a test you run continuously instead of a surprise you find in production.
Read moreCompare GPT-5.6 Sol and Terra on coding benchmarks, pricing, code review, and agent cost. Learn how production traces and evals reveal which model actually solves more work.
Read moreLearn how to design an embeddable payments SDK for an AI app with credit wallets, secure browser sessions, usage debits, webhooks, idempotency, and cost reconciliation.
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