A concrete, do-this-then-that workflow for prompting AI models to generate strong hypotheses, with each step you can follow on a real problem today.
A first-principles introduction to using AI prompts to generate hypotheses, written for people who have never tried it and want to build real confidence.
A working checklist for shipping calibrated AI prompts in 2026, with a short justification for every item so you understand why each gate exists.
A practical model for costing model-assisted hypothesis generation, estimating its benefit through cycle time and hit rate, and presenting a payback case a skeptical decision-maker will accept.
A narrative account of a support team that retooled its AI prompts to fit different readers, the decisions they made, and what changed as a result.
The move underway in 2026 is from one-off idea dumps toward instrumented, evidence-grounded hypothesis pipelines that connect models to data, tests, and feedback loops.
Generating candidate hypotheses with a model is easy. Knowing whether the output is any good takes a deliberate set of metrics covering yield, novelty, testability, and downstream hit rate.
Concrete walkthroughs of audience-adaptive prompts in action, showing exactly what each prompt asked for and why the adapted version landed.
A fast, credible path to running your first structured iterative prompting loop, including the prerequisites, the exact first session, and how to know it worked.
A named, reusable model for instructing models to cite sources, broken into four stages with clear triggers for when each one applies to your workflow.
A research team kept catching invented citations in AI-assisted briefs. This is the narrative of how they diagnosed the problem, redesigned their prompts, and measured the turnaround.
Concrete scenarios across prose, code, data, and legal copy showing exactly what made each error-detection prompt succeed or fail, with the prompts themselves.
Abstract advice about model citations only goes so far. These five concrete scenarios show exactly what good attribution looks like, where it breaks, and why each outcome happened.
A personal knack for catching AI mistakes is fragile. This shows how to convert it into a written, repeatable, hand-off-able workflow with defined inputs, steps, and outputs.
Generic advice about citations is everywhere and helps no one. These are hard-won, opinionated practices for getting models to attribute claims reliably—with the reasoning behind each.
Most citation failures are not exotic. They are the same handful of mistakes repeated until a fabricated reference reaches a decision. Here is each one, why it happens, and the fix.
A concrete, sequential process for getting a model to attribute claims to real sources—from assembling the source set to verifying the output—that you can run on a real task today.
New to making models attribute their claims? This plain-language introduction defines the terms, explains why citations matter, and walks you through your first grounded prompt.
A definitive walkthrough of how to make a model attribute its claims to actual sources—what citations can and cannot guarantee, the prompt patterns that work, and how to verify them.
The cost of generating a hypothesis is collapsing while the cost of testing one is not. That asymmetry is reshaping how teams reason. Here is where prompting for hypothesis generation is heading.
A technique that lives in one person's head is a liability. Here is how to document idea generation with models into a repeatable workflow anyone on the team can run and improve.
The dangerous risks in numerical reasoning are not the obvious wrong answers — they are the confident, plausible, undetected ones. Here is what to watch for and how to contain it.
How to quantify the cost, benefit, and payback of disciplined iterative prompting, and how to present that case to a decision-maker who controls the budget.
Opinionated, reasoned practices for tuning prompts to an audience, each with the thinking behind it rather than generic advice you can skip.
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