Valid JSON is not enough: evaluate structured LLM outputs
Test syntax, schema, field meaning, and downstream behavior separately so machine-readable output is also correct and useful to the consuming system.
Research briefing · Sources includedResearch & field notes
Research library — page 10
Read a guide, follow its sources, and check the limits before applying a finding. Search the full library from the research desk, or browse by topic.
Evaluate an LLM model migration before switching production
Compare a candidate model on your tasks, judge calibration, output contracts, and operational limits before relying on public benchmark improvements.
Research briefing · Sources includedMonitor LLM production drift without collecting everything
Track changes in user requests, answer quality, and scoring rules. Use focused monitoring to turn real failures into better LLM tests while limiting data collection.
Research briefing · Sources includedWhat should an AI evaluation engagement deliver?
A buyer’s guide to evaluation scope, datasets, calibrated scoring, failure analysis, reproducibility, and a handoff your engineering team can use.
Research briefing · Sources includedHow to choose your first B2B AI use case
Choose an AI use case with measurable value, available evidence, and manageable failure costs. A practical scorecard for B2B product teams.
Research briefing · Sources includedChatbot, workflow, or AI agent: which should you build?
Compare chatbots, fixed AI workflows, and agents by task completion, tool use, latency, and recovery. Choose the right level of autonomy for your product.
Research briefing · Sources includedRAG vs. fine-tuning: diagnose the problem first
Understand when retrieval, prompting, or fine-tuning addresses an AI failure. Compare evidence access, behavior consistency, update costs, and evaluation metrics.
Research briefing · Sources includedAI pilot success metrics that survive a real rollout
Measure an AI pilot with accepted outcomes, net handling time, repeat use, and operational costs. Learn why demo accuracy alone cannot justify a rollout.
Research briefing · Sources includedTokens and context windows: a practical guide for teams
Learn how tokens, context limits, output budgets, and conversation history affect AI cost and reliability. Includes a measurement plan for long-document tasks.
Research briefing · Sources includedPrompt caching: measure the savings, not just the hit rate
Understand prompt caching, cache-read metrics, and cost per accepted answer. Learn how to test real savings without confusing cached input with cached responses.
Research briefing · Sources includedModel routing: lower AI cost without hiding quality losses
Design a model-routing experiment with route accuracy, escalation, task quality, and full cost accounting. Learn when a smaller-model path earns its place.
Research briefing · Sources includedHuman handoff in AI products: design and measure the last mile
Build AI handoffs that preserve context and make the next action clear. Measure escalation recall, review effort, and resolution instead of deflection alone.
Research briefing · Sources includedDoes an AI SaaS use your data? Questions that need evidence
Inspect AI SaaS data flows, training-use settings, retention, access, and deletion. A practical evidence checklist with coverage metrics for product teams.
Research briefing · Sources includedBuild or buy an AI product: an evidence-first checklist
Compare building and buying AI software with task quality, integration effort, data controls, total cost, and exit readiness. Includes a practical scorecard.
Research briefing · Sources includedGPT-6 Astra, Sol, and Luna: an evidence-based model guide
Compare current OpenAI GPT-6 models with dated API prices, context limits, and independent benchmark results. A practical selection guide for B2B teams.
Research briefing · Sources includedClaude Opus 5.5: pricing, metrics, and what to test
Review Claude Opus 5.5 with dated pricing and independent planning metrics. Learn how to test coding quality, reviewer effort, and application permissions.
Research briefing · Sources includedMeta Muse Spark 1.3: metrics, pricing, and data-use tiers
Review Meta Muse Spark 1.3 with dated math benchmarks, API pricing, context limits, audio caveats, and the difference between Standard and Contributor tiers.
Research briefing · Sources includedKimi K3: benchmark evidence and a practical evaluation plan
Examine Kimi K3 with independent science and math metrics, API prices, and context limits. Learn how to evaluate long tasks without confusing model and agent.
Research briefing · Sources includedGemini 3.8 Flash: metrics, pricing, and migration checks
Evaluate Gemini 3.8 Flash using dated science and math results, context limits, introductory pricing, and practical migration and multimodal checks.
Research briefing · Sources includedLLM security and bot attacks: a practical test plan
Separate prompt injection from automated bot abuse. Build a practical test plan for access boundaries, tool permissions, cost limits, and evidence-backed fixes.
Research briefing · Sources included