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Find your workflow →Opinionated, hard-won practices for getting dependable numerical output from language models, with the reasoning behind each one rather than generic advice.
Decomposition prompting is becoming a hireable skill. Here is the demand behind it, a realistic learning path, and how to prove the competence in interviews.
Hard-won practices for contrastive prompting that survive contact with real inputs: isolate one variable, mine real failures, validate on the boundary, and document the rule.
Most decomposition advice is generic. These are hard-won, opinionated practices for breaking complex tasks into prompts, each with the reasoning behind it.
Cultural failures hide inside aggregate metrics. Here are the KPIs that actually reveal them, how to instrument each one, and how to read the signal before users walk.
The specific errors that lead language models to produce wrong numbers, why each one happens, what it costs you, and the corrective practice that fixes it.
Decomposition prompting fails in predictable ways. Here are seven failure modes, why each happens, what they cost you, and the corrective practice for each.
Expert-level decomposition prompting, dynamic step counts, parallel branches, recombination, error propagation, and the edge cases that break naive chains.
A named, five-stage model for prompt sensitivity and robustness testing, Specify, Collect, Operate, Rate, Evolve, with guidance on when each stage applies.
Contrastive prompting fails in predictable ways: strawman negatives, muddled pairs, overcorrection, and more. Each failure mode, why it happens, its cost, and the fix.
A concrete, sequential process for prompting language models on numerical tasks, from framing the problem to verifying the result before you act on it.
A concrete, sequential process for disambiguating prompts with contrast: spot the ambiguity, collect real failures, craft paired examples, and validate the boundary.
A practical first path into decomposition prompting: the prerequisites, a four-step starter chain, and how to reach a real working result in an afternoon.
A working checklist for prompt sensitivity and robustness testing, each item paired with the reasoning that makes it worth doing rather than skipping.
A structured walk through the questions practitioners actually ask about adversarial prompt testing, from where to start to when a prompt is safe enough.
A plain-language introduction to why AI models stumble on math and the first techniques anyone can use to get numbers they can actually rely on.
Chain-of-thought, code execution, and program-of-thought each trade accuracy against cost, latency, and auditability. Here is how to decide which one your task needs.
A grounded cost-and-benefit walkthrough of decomposition prompting, with payback math, the failure costs it removes, and how to pitch it to a budget owner.
A first-principles introduction to contrastive prompting for disambiguation, assuming no prior knowledge, building from why models misread instructions to your first contrast.
A survey of the tooling landscape for adversarial prompt stress testing, with selection criteria, category trade-offs, and guidance on how to choose for your stakes.
Should you build a culturally neutral prompt or many localized variants? Here are the competing approaches, the axes that actually decide it, and a rule for choosing.
Where tone and register control is heading: models that infer audience and adapt voice automatically, and what that shift means for how practitioners specify and verify output.
A thorough reference on contrastive prompting for disambiguation: what it is, why showing what you do not want clarifies intent, and how to build robust contrasts.
A structured walkthrough of how to prompt language models for numerical reasoning, from why they fail at arithmetic to the techniques that make their math dependable.
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