Vibe Engines — AI Evaluation

Design an Enterprise Eval Pipeline

Sat, 01 Aug 2026 00:00:00 GMT

Turn “it should be accurate” into a gate that blocks a merge. Learn a stratified frozen golden set, the inter-expert agreement ceiling that sets a defensible target, an LLM judge reported with its calibration, a CI gate asserting per-case regressions rather than an average, drift monitoring that separates distribution shift from score decay, and the one business number the renewal actually turns on.

Evals for Client Work

Fri, 31 Jul 2026 00:00:00 GMT

The most-cited hard skill in frontier-lab FDE job descriptions and the least taught anywhere: turning \u201cit should be accurate\u201d into a gate. Measure inter-expert agreement before you promise a number, build a golden set that survives contact, calibrate an LLM judge against human grades, wire the CI gate (including the per-case regression check), and hand the whole thing over.

Eval Builder: From Fuzzy Goal to CI Gate

Fri, 31 Jul 2026 00:00:00 GMT

A customer says the system \u201cshould be accurate\u201d. Turn that into something a contract can rest on, in four acts: pick metrics (two of the strongest are not accuracy at all), discover the real ceiling by measuring how often the two human experts agree with each other, configure and calibrate an LLM judge, then set a CI gate and watch it catch a change that raises overall accuracy while more than doubling the expensive errors. The most-cited hard skill in FDE job descriptions, made playable.

Self-Improving Agents

Sun, 26 Jul 2026 00:00:00 GMT

The long one: how an agent gets better at its own job, end to end. COVERAGE vs the accuracy you can ship (a model usually already produces a right answer somewhere in its output; the missing piece is recognising it), unbiased pass@k, why the sampling curve is straight, the SELECTION bottleneck, building a VERIFIER and the two asymmetric ways it is wrong, feedback that is not a verifier ranked by independence, the five-predicate filter that decides what becomes training data, the SIX substrates a lesson can land in (context, memory, tools, scaffold, verifier, weights) and how to choose, STaR through PPO/GRPO/DPO and reward hacking, why a loop refit on its own output COLLAPSES toward its own habits, search as self-improvement, and measuring a time HORIZON rather than a score. 14 sections, 13 runnable Python blocks, 3 interactive models, 13 diagrams, 5 checkpoints and a running build log. Every figure carries the condition it was measured under.

AI Red-Teamer Roadmap

Sun, 26 Jul 2026 00:00:00 GMT

A step-by-step roadmap to become an AI red-teamer in 2026 — the offensive side of AI security, explicitly distinct from the defensive AI-safety and classic security-engineer tracks. From the AI attack surface, threat modeling and red-team tooling through prompt injection, jailbreaks, data poisoning, model extraction and agent exploitation to injection defenses, gateways, supply-chain attacks, continuous red-teaming and responsible disclosure. 18 stations across 3 tracks — Foundations, Attack Techniques, Defense & Career.

QA / SDET Roadmap

Fri, 24 Jul 2026 00:00:00 GMT

A step-by-step roadmap to become a QA engineer or SDET in 2026. From the test pyramid, unit and integration testing and test design through UI and API automation, frameworks, CI pipelines and flaky-test control to performance, security, testing AI systems and the SDET career. 18 stations across 3 tracks — Testing Foundations, Automation, Advanced Quality.

AI Safety Engineer Roadmap

Fri, 24 Jul 2026 00:00:00 GMT

A step-by-step roadmap to become an AI safety engineer in 2026. From alignment, evaluation, interpretability and threat modeling through prompt injection, guardrails, data poisoning, adversarial robustness and agent safety to policy, model documentation, responsible deployment and scalable oversight. 18 stations across 3 tracks — Foundations, Securing AI Systems, Governance & Frontier.

A/B Test Significance Calculator

Fri, 24 Jul 2026 00:00:00 GMT

An A/B test significance calculator using a two-proportion z-test. Enter the visitors and conversions for a control and a variant to get each conversion rate, the relative uplift, the z-score and two-tailed p-value, and a clear verdict on whether the result is statistically significant. Explains what significance does and does not tell you.

The Evals in CI Handbook

Mon, 20 Jul 2026 00:00:00 GMT

Turn LLM quality into a regression gate, like unit tests for code. A golden set scored on every change, and why the gate needs both an aggregate threshold AND per-case no-regression — because a healthy average can hide a broken case (mean stays up while one case collapses). How to wire it into the PR pipeline, handle non-determinism with a tolerance band, and avoid a flaky gate. With worked math and runnable code.

Design a Model Registry (MLOps)

Mon, 20 Jul 2026 00:00:00 GMT

Build the platform that versions, evaluates, promotes and can instantly roll back trained ML models — the governance layer between "a model finished training" and "a model is safely serving production traffic." See why an artifact alone is meaningless without lineage, staged promotion through gated environments, automated evaluation gates that fail closed, shadow/canary deployment because offline metrics aren't sufficient, instant rollback via immutable versioning, and production drift monitoring.

Design a Data Labeling Platform

Mon, 20 Jul 2026 00:00:00 GMT

Build a human annotation platform like Scale AI or Labelbox — sourcing labels from people, not generating them programmatically. See why a single annotator's answer can't be trusted as ground truth, multi-annotator consensus, secretly-mixed gold-standard items that measure labeler accuracy in real time, skill-based task routing, active learning to prioritize which unlabeled examples matter most, and aligning labeler pay with measured quality.

Design a Prompt Management & A/B Testing Platform

Mon, 20 Jul 2026 00:00:00 GMT

Build the platform that versions, evaluates, and safely A/B tests prompts in a production LLM application — a much faster-moving artifact than model weights, often edited by non-engineers. See why hardcoded prompts force a full deploy for every tweak, externalizing prompts as versioned config, an offline eval gate before rollout, live A/B testing, guardrail metrics that must not regress, routine one-click rollback, and a review gate suited to non-engineer editors.

Design an AI Ad Creative Platform

Mon, 20 Jul 2026 00:00:00 GMT

Build a platform that generates ad creative at scale for advertisers, built on top of generative models rather than serving them directly. See why one hand-designed creative can't be tested at scale, programmatic variant generation, a brand-safety and ad-policy compliance gate that fails closed, rights and licensing tracking, dynamic multi-armed-bandit budget allocation, a performance feedback loop, and tiered generation cost control.

Design a Support Resolution Agent

Mon, 20 Jul 2026 00:00:00 GMT

Build an AI agent that triages, resolves, and escalates customer support tickets end-to-end. See why answering from general knowledge alone is dangerous when the real answer depends on account-specific state, grounding responses in real account/order data, confidence-based escalation instead of guessing, tiered auto/assisted/escalate resolution, seamless handoff context, outcome-based quality tracking, tone-aware de-escalation, and identity verification against social engineering.

Design a CI Test-Generation Agent

Mon, 20 Jul 2026 00:00:00 GMT

Build an agent that automatically generates tests for new and changed code in a CI pipeline. See why manual tests leave coverage gaps, coverage-gap analysis, generating tests against the real code, mutation testing to verify a test is actually meaningful, flaky-test detection before it poisons CI signal, why a human review gate is still required, and running expensive validation in a background lane.

Design LLM Eval & Observability

Wed, 01 Jul 2026 00:00:00 GMT

Build an LLM evaluation and observability pipeline. See how offline evals and online observability form two loops, how tracing every call is the foundation, how versioned golden datasets become your real benchmark, how programmatic/LLM-judge/human scorers combine, how a CI regression gate blocks regressions, how production monitoring catches drift, how failures feed back as fixtures — and why an unvalidated LLM judge silently corrupts every metric — through an interactive diagram.

Token-Level F1

Tue, 30 Jun 2026 00:00:00 GMT

The metric behind QA evaluation (SQuAD and friends): how well does a predicted answer overlap a reference as a bag of words? Compute token precision and recall, then their harmonic-mean F1. Solve it in Python or TypeScript.

LLM-as-Judge Rubric Builder

Wed, 17 Jun 2026 00:00:00 GMT

Define your evaluation criteria and a scoring scale, then generate a clean, copy-pasteable LLM-as-judge prompt you can drop into your eval pipeline — with the common pitfalls (position bias, verbosity bias, ties) called out. Turns eval theory into a prompt you can ship.

The Agent Evaluations Handbook

Mon, 04 May 2026 00:00:00 GMT

A self-contained handbook on evaluating AI agents — theory, interactive widgets, and practical guidance. Trajectory evals, tool-use scoring, LLM-as-judge, observability, and reliability for PMs, engineers, and founders.

51 LLM Evals Interview Questions

Thu, 23 Apr 2026 00:00:00 GMT

Golden sets, LLM-as-judge, regression testing, offline vs online evals, RAG evals, agent evals, red-teaming, and observability — demystified for interviews and production.