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What “contextual continuity” means
Schmarzo defines contextual continuity as “the ability of a Generative AI (GenAI) system, such as ChatGPT, to use, generate, and retain relevant information to produce more pertinent, meaningful responses.” In practice, the user supplies the situation, purpose, local knowledge, conversation structure, and periodic summaries that help the model stay on task.
The method applies to ChatGPT, Copilot, or another GenAI system, but it does not turn the system into an agronomist. A persona prompt changes framing and depth; it does not provide professional credentials, field observations, or verified local data.
The five-part workflow
1. State the problem and desired outcome
Begin with a decision, not a broad request for ideas. Describe the farm, the decision deadline, constraints, and what a useful answer must contain. Schmarzo compares this to briefing a consultant or explaining a research need to a librarian.
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For the farming example, the opening request would identify a 1,000-acre Northeast Iowa operation choosing spring crops and ask for a structured analysis of alternatives, assumptions, risks, and information still needed.
2. Provide relevant local and organizational knowledge
General models may not know the farm’s rotation history, soil types, drainage, labor capacity, equipment, water availability, contracts, storage, or nearby buyers. Supply those details directly, along with documents or notes the model is allowed to use.
Schmarzo calls this kind of local, organization-specific information “tribal knowledge” and connects it with his “Thinking Like a Data Scientist” methodology. The more specific the input, the less the conversation depends on generic assumptions—but every supplied fact still needs to be accurate and current.
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3. Build a narrative with sequenced questions
Lead the model through related questions instead of issuing disconnected prompts. Start with the decision criteria, examine candidate strategies, test trade-offs, and then probe risks and missing evidence. Schmarzo relates this progression to the Socratic Method and his “Nine Categories of GenAI Innovation.”
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Sequencing preserves the thread of the decision. After each answer, ask the next question using the conclusions, uncertainties, and assumptions already identified.
4. Request a useful perspective
Ask for a defined perspective—such as a soil scientist or sustainability consultant—to guide terminology, depth, and the questions the model raises. Treat this as a framing instruction only. It does not establish that the model has that person’s training, licensing, or access to professional judgment.
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5. Refine and summarize periodically
At natural checkpoints, ask the tool to consolidate the discussion, preserve confirmed facts, flag assumptions, correct drift, and list unresolved questions. This creates a working brief that can be checked against the original objective before the conversation grows longer.
How the farming example defines success
The hypothetical farmer is not optimizing a single number. Schmarzo’s proposed objectives are:
| Objective | What the conversation should examine |
|---|---|
| Profitability | Expected revenue and costs, while identifying which figures require current farm and market data. |
| Climate adaptation | How crop choices might respond to climate variability and weather uncertainty. |
| Soil health | Rotation, nutrient management, and longer-term effects on the soil. |
| Resource efficiency | Use of water, fertilizer, labor, and available equipment. |
| Risk reduction | Exposure to volatility, operational disruptions, and unfavorable outcomes. |
| Market alignment | Whether prospective crops fit relevant market trends and the farm’s actual market access. |
A useful response should make conflicts visible. A crop that appears attractive on price may require more water, labor, or fertilizer; a rotation benefit may matter over several seasons rather than in one spring.
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Using “What If” scenarios without treating them as forecasts
The article carries the conversation from planning into structured risk exploration. Its tariff example imagines the United States imposing 50% tariffs on agricultural imports from Canada and Mexico, followed by equivalent retaliatory tariffs on U.S. exports. That rate and policy setup belong to the hypothetical; they are not presented here as current policy or a verified market forecast.
Under that scenario, prompts could ask:
- How might export demand and domestic prices change under the stated assumptions?
- Could another crop become relatively more attractive?
- What subsidy or policy adjustments would materially change the decision?
Schmarzo also suggests exploring severe drought, supply-chain disruption, and removal of agricultural subsidies. These are stress-test prompts. Answering them responsibly requires current, local evidence about policy, weather, contracts, logistics, prices, and farm conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical prompt sequence
The following sequence translates the method into a reusable conversation. Replace the bracketed details with verified information rather than asking the model to invent them.
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- Frame the decision: “I operate a hypothetical 1,000-acre farm in Northeast Iowa. I must decide what crops to plant in the spring. Evaluate options against profitability, climate adaptation, soil health, resource efficiency, risk reduction, and market alignment. State assumptions and list the data you need.”
- Add local knowledge: Provide rotation history, soil and drainage information, recent weather, water and fertilizer constraints, labor and equipment limits, storage, contracts, and buyers.
- Ask for a perspective: “Analyze this first as a soil scientist, then identify questions a sustainability consultant would ask. Do not claim professional credentials or replace local agronomic advice.”
- Proceed in stages: Ask the model to rank the decision criteria, compare strategies, expose trade-offs, and identify evidence that could change the ranking.
- Run stress tests: Apply clearly labeled scenarios such as severe drought, supply-chain disruption, subsidy removal, or the article’s hypothetical tariff conditions.
- Reconcile the record: “Summarize confirmed facts, assumptions, conclusions, unresolved questions, and information that must be verified before any planting decision.”
What this example does—and does not—establish
The workflow is an instructional example of context management. It does not compare crop varieties, calculate a Northeast Iowa planting plan, verify trade policy, or supply current prices. It also does not report an empirical test showing that the sequence improves yields, profits, accuracy, or decision quality.
Schmarzo’s terminology has an important limitation: “Technically, you are not ‘training’ your GPT.” In this use, the farmer is supplying relevant information and directing the model’s attention to a problem; that is contextual prompting, not updating the underlying model’s parameters.
Before acting on an AI-generated analysis, verify agronomic, financial, legal, and market claims with current authoritative sources and qualified local professionals. Keep the model’s assumptions separate from measured field data, and treat its output as a draft for scrutiny rather than an operating plan.
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