Contextual continuity is Bill Schmarzo’s practical term for keeping a GenAI conversation anchored to one problem by supplying relevant information, asking connected questions, adopting a useful perspective, and periodically summarizing and refining the exchange. It is a prompt-time workflow—not model training, fine-tuning, or a formally validated technical standard.
What contextual continuity means
Schmarzo defines contextual continuity as a GenAI system’s ability to use, generate and retain relevant information so it can produce more pertinent, meaningful responses. In practice, the user deliberately creates that continuity rather than starting each prompt from scratch.
The approach is about infusing a tool with information that focuses its responses on a particular issue. It does not change the model’s parameters. Calling the process “training” can therefore mislead readers: supplying documents and instructions inside a conversation is not the same as technically training or fine-tuning a model.
The original article was published November 27, 2024. Schmarzo’s substantive follow-up, published February 5, 2025 (with a February 6 update noted in the record), explains the workflow through a farming scenario.
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Schmarzo’s five-step workflow
1. State the problem and desired outcome
Begin with the decision or task, not a broad request for information. Define the scope, objective, constraints and perspective the answer should use.
- Problem: What decision or challenge is being addressed?
- Outcome: What should the response help you decide, produce or compare?
- Scope: Which time period, geography, business unit or user group matters?
- Constraints: What budget, risk tolerance, regulations, deadlines or resources limit the options?
- Perspective: Should the response emphasize cost, resilience, safety, sustainability or another priority?
A specific frame gives later questions a stable target and makes it easier to identify when an answer has drifted.
2. Capture and provide relevant knowledge
Add the documents and organizational or domain knowledge the tool would not otherwise have. Schmarzo calls this information “tribal knowledge.” It might include policies, project notes, specifications, operating assumptions, historical decisions or a glossary of internal terms.
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Identify what each document is, its date and its role. Separate established facts from assumptions and label information that is illustrative. If a source conflicts with another, ask the model to flag the conflict rather than silently choosing one version.
3. Build a narrative
Ask connected questions in a deliberate sequence. Start with framing and facts, move to analysis, then test options and edge cases. A narrative lets each answer become input to the next question instead of repeatedly resetting the conversation to a generic explanation.
- Ask the tool to restate the problem, objectives and constraints.
- Request a structured inventory of the supplied facts, assumptions and unknowns.
- Explore options against the stated objectives.
- Test the leading options with “what if” changes.
- Ask what additional information would change the recommendation.
4. Set a persona or perspective
A persona prompt tells the tool which professional viewpoint, priorities and communication style to simulate—for example, a soil scientist, sustainability consultant, compliance reviewer or financial analyst.
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This is an instruction about perspective, not evidence that the system possesses that person’s credentials, professional liability or firsthand knowledge. Keep the underlying facts and verification requirements explicit even when a persona is useful for organizing an answer.
5. Iteratively refine and summarize
Long exchanges can accumulate irrelevant branches or inconsistent assumptions. Periodically ask for a concise state of the discussion: the goal, confirmed facts, assumptions, open questions, options considered and next decision. Then correct errors and continue from that cleaned-up context.
Summaries are also useful when moving to a new chat or handing work to another person. Treat the summary as a working brief that must be checked against the source documents, not as an authoritative record merely because the model produced it.
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Prompt-time context versus changing a model
| Approach | What changes | What the user supplies | What Schmarzo’s method describes |
|---|---|---|---|
| Prompt-time contextual continuity | The information and instructions available to the current interaction | Problem framing, documents, connected questions, perspective and summaries | Yes |
| Technical training or fine-tuning | Model parameters through a separate training process | Training data, a training procedure and evaluation | No; the author explicitly distinguishes this from his usage of “training” |
The distinction matters for expectations. Context can focus a response during an interaction, but it is not proof that the model has permanently learned the supplied material or that a later conversation will remember it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.The farming example: how the workflow is applied
Schmarzo imagines a farmer managing a 1,000-acre farm in Northeast Iowa and deciding what to plant in spring. The stated objectives include profitability, resilience to climate variability, soil health, efficient resource use, risk reduction and alignment with market trends.
Applied step by step, the conversation would establish those objectives, provide relevant farm and market knowledge, ask connected crop-selection questions, request a soil-scientist or sustainability-consultant perspective, and periodically consolidate the analysis. The farmer could then explore hypothetical conditions such as drought, supply-chain disruption or a 50% tariff.
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These acreage, tariff and crop scenarios are illustrative details in the article, not a reported farm study, market forecast or independently verified recommendation. The tariff case should be treated strictly as a “what if” exercise.
A practical conversation pattern
The following template turns the five steps into a reusable working brief:
- Goal: “Help me decide [specific decision] by [date].”
- Success criteria: “Prioritize [criteria] and show the trade-offs.”
- Context: “Use these facts, documents and definitions: [list].”
- Boundaries: “Do not assume [excluded assumptions]. Mark unknowns and conflicts.”
- Perspective: “Analyze this as a [role], while identifying where specialist verification is required.”
- Sequence: “First restate the brief; then list facts and assumptions; next compare options; finally test the top option under [scenarios].”
- Checkpoint: “Summarize the current brief, evidence, uncertainties and next question before continuing.”
When the conversation drifts, paste the latest verified summary and ask the tool to ignore superseded assumptions. When a response contains a new factual claim, ask which supplied source supports it or label it as an inference to verify.
What this method can and cannot establish
What it is intended to improve
- Relevance to a clearly defined problem rather than a generic topic.
- Continuity across a series of related questions.
- Visibility of assumptions, trade-offs and “what if” conditions.
- Use of organization-specific knowledge supplied by the user.
What the cited material does not prove
- There is no controlled evaluation, benchmark or quantified result showing that contextual continuity improves accuracy, productivity, relevance, return on investment or decision quality.
- There is no independent comparison with other prompting methods.
- The farming scenario is not a measured trial or validated agronomic recommendation.
- The examples of ChatGPT and Copilot do not establish current model versions, features, pricing, privacy terms or suitability.
Accordingly, present the five steps as Schmarzo’s proposed workflow and a useful way to structure an interaction, not as a guaranteed performance intervention.
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Checks before relying on an answer
- Verify that the model’s summary preserves the original objective and constraints.
- Check citations, dates, units and geographic assumptions against the supplied documents.
- Separate sourced facts from model-generated inferences and hypothetical scenarios.
- Ask for missing information and competing interpretations instead of accepting confident guesses.
- Have an appropriately qualified person review decisions involving safety, law, finance, health, agriculture or other material risk.
Contextual continuity is most useful as disciplined conversation management: it helps a user keep a model focused while retaining responsibility for evidence, judgment and final decisions.
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