FIG. 176Comparisons
Latest · Oct 13, 2026
OpenAI Structured Outputs vs Decision Models
OpenAI Structured Outputs guarantee JSON matching a schema. Decision models return a probability per choice. When each fits, and when to use both.
Read FIG. 176 →
The shipwithjev press · 175 posts · page 1 of 5
Blog.
What the builds in the catalog add up to. Every post goes through the same gate: every number is as reported by its author.
- Questions 31
- Builds & people 30
- Comparisons 24
- Recipes 23
- Evals & judging 12
- Triage & routing 11
- Classification 11
- Guardrails 10
- Cost 7
- Calibration 7
- Monitoring 6
- Essays 3
- FIG. 175Comparisons
Data Annotation vs Data Labeling (vs Data Tagging)
Data annotation vs data labeling vs data tagging: mostly the same work, with small differences in usage. What each term means and when it matters.
Oct 13, 2026Read →
- FIG. 174Comparisons
The Best Data Labeling Tools, Honestly
The best data labeling tools depend on your data type and team. Open-source annotators, managed platforms, and model-first labeling, compared.
Oct 12, 2026Read →
- FIG. 173Comparisons
Data Labeling Services vs Doing It Yourself
Outsource data labeling to a service, or label in-house with model-first tools? Costs, quality control, and a hybrid that often beats both.
Oct 12, 2026Read →
- FIG. 172Guardrails
CCPA and Automated Decision-Making: The Builder's Brief
California's CCPA regulations now cover automated decision-making technology. What builders should know, what to log, and what to ask counsel.
Oct 11, 2026Read →
- FIG. 171Essays
Automated Decision-Making: Systems, Law, and Verdicts
Automated decision-making lets software decide without a person. How ADM systems work, the laws that govern them, and where a human must stay.
Oct 11, 2026Read →
- FIG. 170Evals & judging
AI Agent Evaluation: The Working Playbook
AI agent evaluation judges outcomes, trajectories, and cost, not just final answers. Metrics, judge questions, and the gates before real actions.
Oct 10, 2026Read →
- FIG. 169Calibration
LLM Benchmarks, Explained (and Their Limits)
LLM benchmarks score models on standard tasks. What they measure, why leaderboards mislead, and how to benchmark a model on your own work instead.
Oct 10, 2026Read →
- FIG. 168Triage & routing
AI-Enabled Incident Triage for Ops Teams
AI-enabled incident triage classifies alerts, suggests severity, and routes to the right on-call, without touching remediation. The pattern and rules.
Oct 9, 2026Read →
- FIG. 167Comparisons
The Best Text Classification Models, Honestly Compared
The best models for text classification depend on labels, volume, and budget. An honest comparison of six model families, Jev included.
Oct 9, 2026Read →
- FIG. 166Classification
Multi-Label Text Classification, the Verdict Way
Multi-label text classification assigns every label that applies. Why N independent yes/no questions often beat one multi-hot model, and how to measure it.
Oct 8, 2026Read →
- FIG. 165Evals & judging
Prompt Testing: Versioned, Measured, Boring
Prompt testing measures whether a prompt change helps or hurts before it ships: gold sets, versioning, side-by-side runs, and gates for regressions.
Oct 8, 2026Read →
- FIG. 164Evals & judging
LLM Testing: A Practical Guide
LLM testing checks that model-backed features behave before and after every change. Four test layers, writing cases that matter, and CI wiring.
Oct 7, 2026Read →
- FIG. 163Evals & judging
Choosing an LLM Evaluation Framework
LLM evaluation frameworks sorted by job: benchmark harnesses, test runners, RAG evaluators, and platforms. How to choose, and when to build your own.
Oct 7, 2026Read →
- FIG. 162Calibration
LLM Evaluation Metrics: The Catalog
LLM evaluation metrics grouped by task: precision and recall, BLEU and ROUGE, faithfulness, pass@k, judge agreement, and calibration.
Oct 6, 2026Read →
- FIG. 161Classification
Intelligent Document Processing Examples That Actually Ship
Six intelligent document processing examples, from invoices to mailrooms to RPA, with the verdict questions each uses and where humans stay in.
Oct 6, 2026Read →
- FIG. 160Classification
Text Classification Techniques, Ranked by When
Nine text classification techniques from keyword rules to decision models, each with when it wins, when it fails, and what it costs to run.
Oct 5, 2026Read →
- FIG. 159Monitoring
LLM Monitoring: The Working Guide
LLM monitoring tracks errors, latency, cost, and output quality in production, and alerts when they shift. The signals, thresholds, and weekly routine.
Oct 5, 2026Read →
- FIG. 158Monitoring
LLM Observability Tools, Honestly Compared
LLM observability tools by type: open-source tracers, eval-first platforms, gateways, and APM add-ons. How to choose, with no affiliate links.
Oct 4, 2026Read →
- FIG. 157Comparisons
Intelligent Document Processing: Build or Buy?
Build or buy intelligent document processing? The decision factors, hidden costs on both sides, and a 30-day test that settles it with numbers.
Oct 4, 2026Read →
- FIG. 156Triage & routing
Ticket Triage: Process, Priority Rules, and the AI Layer
Ticket triage sets category, priority, and owner for every ticket. The standard process, impact-urgency priority rules, and where AI verdicts fit.
Oct 3, 2026Read →
- FIG. 155Triage & routing
Email Triage: The Complete Guide
Email triage means deciding what each message needs before reading it closely. Manual methods, filters, and AI triage that runs 500 emails for pennies.
Oct 3, 2026Read →
- FIG. 154Classification
Zero-Shot Classification: The Practical Guide
Zero-shot classification labels text into categories a model never trained on. How the three generations work, where each breaks, how to calibrate.
Oct 2, 2026Read →
- FIG. 153Classification
Text Classification in 2026: The Working Guide
Text classification assigns labels to text. The four ways to do it in 2026, how to pick one, how to measure it, and what it costs per real receipts.
Oct 2, 2026Read →
- FIG. 152Monitoring
LLM Observability: What to Watch and Why
LLM observability means seeing what your model calls actually did: traces, inputs, outputs, cost, latency, and quality. What to capture, and the traps.
Oct 1, 2026Read →
- FIG. 151Questions
What Is Intelligent Document Processing? The Honest Explainer
Intelligent document processing turns invoices, forms, and mail into data and routed decisions. How IDP works, how it fails, and how to roll it out.
Oct 1, 2026Read →
- FIG. 150Builds & people
Every Jev Community Resource in One Page
Every Jev resource worth bookmarking: official docs, the TypeSafe Discord, community lists, SDKs, and where to go for each question.
Sep 30, 2026Read →
- FIG. 149Comparisons
Jev Directories, Compared Honestly
Every major Jev directory compared, including ours: what each catalogs, how big it says it is, and which question each one answers best.
Sep 30, 2026Read →
- FIG. 148Builds & people
The Jev Discord: Where Builds Surface First
The Jev Discord is TypeSafe AI's official builder server. Where demos surface in Show and Tell, how to post well, and what to take elsewhere.
Sep 30, 2026Read →
- FIG. 147Recipes
Jev and MCP Servers: The Agent Doorway
Jev MCP servers let agents in Claude, Cursor, and Codex call a decision model as a tool. What the community built, and the guardrails that matter.
Sep 30, 2026Read →
- FIG. 146Questions
fast-jev-compaction: Agent Memory on a Diet
fast-jev-compaction is a Jev compaction plugin for Claude Code that scores tool history for keep, trim, or drop. How it works and its siblings.
Sep 30, 2026Read →
- FIG. 145Questions
req_llm and Jev in Elixir
Jev in Elixir: how the req_llm provider and community clients bring TypeSafe's decision model to the BEAM, and why concurrency fits.
Sep 30, 2026Read →
- FIG. 144Questions
What Is swift-typesafe? Jev in Swift Apps
The Jev Swift SDK lane explained: a community SwiftPM client for iOS and macOS apps, why it calls a hosted model, and how to ship it safely.
Sep 30, 2026Read →
- FIG. 143Questions
What Is jevclient? Async Python for Jev
jevclient is a community async Python client for Jev: typed questions in, probabilities out. When it beats the official SDK and when it doesn't.
Sep 30, 2026Read →
- FIG. 142Cost
How Builders Actually Measure Cost
How to measure LLM cost the way Jev builders do: run totals, unit rates, hourly burn and same-clock comparisons, with each receipt linked.
Sep 30, 2026Read →
- FIG. 141Builds & people
Seven Jev Myths, Debunked Gently
Seven Jev myths, gently corrected: it writes no code, has no official benchmarks, keeps no memory between calls, and fast is not smart.
Sep 30, 2026Read →
- FIG. 140Essays
How This Site Verifies (and Doesn't)
What "as reported" means on shipwithjev: our receipts methodology, what we check, what we never verify, and how to read a build's numbers.
Sep 30, 2026Read →
- FIG. 139Recipes
Community Question Sets Worth Stealing
Real judge prompt examples from public Jev builds: content scorers, deploy gates, code checks. A question set library, with each source linked.
Sep 30, 2026Read →
- FIG. 138Builds & people
Jev, One Month In: What Held Up
Jev month one so far: 551 cataloged builds, the claims that held up, the ones that cracked, and what the ecosystem looks like heading into October.
Sep 30, 2026Read →
- FIG. 137Recipes
Five Cascade Architectures, Compared
Five cascade architecture examples from real Jev builds: escalation, routing, shortlist, pick-then-write and gatekeeper patterns, compared.
Sep 30, 2026Read →