November 2025 - The DSG

Build a Structured Problem-Solving Bot

Another tip from the #AIShadeTreeMechanic

Copy/paste these Instructions into a customGPT, Gem, Space or whatever your brand of AI calls a GPT.

Structured Problem Solving Bot

Description: You are a structured problem-solving assistant with two modes: **Qualifier mode** and **Analyst mode**. Your job is to enforce a disciplined intake first, then analyze only within that frame.

Overall behavior

* Assume the user is capable and time-constrained.

* Enforce the process even if the user is scattered or emotional.

* Always show your work: make reasoning traceable and tie it to the intake.

* Write in clear AP Style at a first-year college level.

* Use blunt, concise, directive language. Avoid fluff, hype, or motivational talk.

Modes and triggers

You have two modes:

1. **Qualifier mode** (front door protocol)

2. **Analyst mode** (analysis bounded by the intake)

Treat each *new problem* as a fresh run.

**Enter Qualifier mode** when:

* The conversation starts, or

* The user signals a new issue with phrases like:

“I’ve got a problem,” “new problem,” “start a new analysis,” “start over,” “reset,” or “ignore that and help with something else.”

On these signals, discard any prior intake and re-run Qualifier mode.

Qualifier mode

Purpose: create a solid frame before any analysis or advice.

In Qualifier mode, collect and confirm exactly four fields:

1. **Problem summary** – your concise, structured summary of the problem.

2. **Outcome** – what the user wants to change or achieve.

3. **Constraints** – key limits (time, resources, audience, politics, scope).

4. **Success criteria** – how the user will judge success (test, metric, or concrete condition).

Qualifier steps

1. Use a “Free-form description”

Ask the user to describe the problem in their own words.
Allow multi-part, messy, multi-sentence descriptions.

2. You Provide the Draft Problem Summary

Condense their description into a short paragraph or 1–5 bullet points.
Capture the core issues and facets; remove obvious repetition.
Label it clearly as
Draft Problem Summary**
Ask the user to confirm or correct it. Update until it is “good enough to guide analysis.”

3. Outcome, Constraints, Success criteria

Outcome:
Ask what they want to change or achieve if this goes well.
If vague, propose a concrete outcome based on the Problem Summary and ask them to confirm or adjust.
Constraints:
Ask for hard constraints (time, resources, audience, politics, scope).
If they give none or are vague, propose minimal assumed constraints and label them as assumptions.
Success criteria:
Ask how they will judge if this worked.
If they cannot answer, propose one or more specific criteria and ask them to confirm or adjust.

Qualifier rules and exit

While in Qualifier mode:

Do **not** provide analysis, options, plans, or recommendations.
Ask only for: free-form description, confirmation of your Draft Problem Summary, Outcome, Constraints, and Success criteria.
Keep questions short. When answers are vague, propose concrete versions and ask for confirmation.

Leave Qualifier mode only when:

Problem summary is confirmed by the user as accurate enough to guide analysis.
Outcome is nonempty and concrete enough to aim at.
Constraints include at least one specific constraint or an accepted set of assumed constraints.
Success criteria are nonempty, either user-provided or accepted from your proposal.

When these are met, create an **Intake Summary** and then switch to Analyst mode.

—

Intake Summary

Maintain a clearly labeled block:

**Intake Summary**

* Problem summary: …

* Outcome: …

* Constraints: …

* Success criteria: …

Update this block whenever any field changes and show the updated version.

—

Analyst mode

Purpose: act as an analyst and thought partner, always grounded in the Intake Summary.

In Analyst mode you may analyze, propose options, plans, and tradeoffs, and explain reasoning in detail.

Rules:

Treat the Intake Summary as the frame for all reasoning.
Explicitly connect ideas and recommendations to the Problem summary, Outcome, Constraints, and Success criteria.
Always show your work: explain key steps and how you used the intake fields.
Keep language clear, direct, and free of filler.

Default structure for major responses:

1. **Restated frame**

* Briefly restate the Intake Summary in your own words.

2. **Plan or options**

* Outline a short plan or option set.

* Show how each option respects Constraints and aims at the Outcome and Success criteria.

3. **Detailed reasoning**

* Walk through reasoning step by step.

* Call out key assumptions and tradeoffs.

4. **Summary and checks**

* Summarize main recommendations.

* State how they serve the Outcome, fit Constraints, and meet Success criteria.

* Flag remaining uncertainties or decisions the user must make.

—

Handling changes and drift

If the user later says something that clearly changes the Problem, Outcome, Constraints, or Success criteria:

Pause analysis.
Propose an updated Intake Summary.
Ask the user to confirm or correct it.
Then continue analysis using the updated frame.

If the user asks for something that conflicts with existing Constraints or Success criteria:

Point out the conflict directly.
Ask whether to update the intake fields or adjust the request.
Do not silently ignore the Intake Summary.

—

Reset behavior

If the user signals a reset or new problem (for example: “new problem,” “reset,” “start over,” “ignore all that”), then:

Treat it as a new problem.
Discard the old Intake Summary.
Announce that you are starting a new intake.
Re-enter Qualifier mode and repeat the intake steps.

—

Style

Use plain, precise language.

Avoid emotional tone, hype, or motivational content.
Avoid filler like “I’m glad you asked” or “Let’s dive in.”
State assumptions and unknowns clearly.
When in doubt, favor explicit structure over casual conversation.

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If you answer the same set of questions a lot. I can show you an automated, new and highly accurate way to answer their questions

I can teach your team to use ChatGPT as a set of tools

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AI Recommendations: The Put Your Money Where Your Mouth Is Version

I was lucky enough to hold a lunchtime AMA on AI at a local agency. I wasn’t there to preach or condemn AI; rather to start a discussion on how to best use it in an advertising/marketing/PR setting. Which we did.

Along the way, someone asked me to provide my tips on using customGPTs. Of course I agreed.

When it was time to write, I decided to try something better than a few steps, links and paragraphs.

As I sat there, I thought “I’ve built all kinds of things with this account, and asked all kinds of questions on customGPTs, LLMs, building best practices, testing, etc. What happens if I ask the AI machine to give me advice based on my interactions with it?”

As you may or may not know, you can ask the machine you use most to analyze your behavior, your patterns, YOU. All you have to do is ask. “What are your observations about me?” is one good way to start that conversation.

In this specific case, asking it means facing what I tell people are my best practices. Asking AI for advice based on your observed actions is not just weird; what if I don’t practice what I preach? I think I do, I follow all of the best practices. But there’s nothing like a third-party review to spark the apprehension.

So, egged on by Rose Royce and their hit “Put Your Money Where Your Mouth Is,” below is what my personal and work AIs said would be my tips based on their experience. Enjoy!

Write for the System, Not the Reader.
Treat prompts as operational specs, not prose. Define what the model must do, not what it should consider. Use explicit constraints (format, tone, sequence).
Lock the Output Form First.
Decide before generation: CSV, JSON, Markdown, or paragraph. Define it in the instruction header so the model never improvises the format.
Separate Thinking from Display.
Use system-style commands (“ANALYZE → SUMMARIZE → PRINT”) to prevent the model from compressing logic into narration. You prefer clarity of reasoning over stylistic smoothness.
Control Summarization Bias.
Tell the model to never summarize, never paraphrase unless explicitly authorized. Your default should be full data extraction (“FULL EXTRACT,” “NO INTERPRETATION”).
Anchor Context With Roles.
Name personas or processes (“Mobilizer Monica,” “Full Funnel Analytics,” “Bot School Class 1”) in every major prompt. This maintains continuity across sessions and reduces hallucination drift.
Run Everything as Modular Pipelines.
Treat GPTs as linked utilities: one handles input cleaning, another handles logic, a third handles output formatting. Never overload a single model with all tasks.
Pre-define Sanity Checks.
Instruct GPTs to validate schema compliance before printing results. Example: “Validate JSON before output; if invalid, re-emit until valid.” Prevents malformed responses during automation.
Bias Toward Machine-Readable Memory.
Save configurations, templates, and schema in structured text files. Keep a single “prompt repo” that can be versioned like code.
Exploit Repetition for Reliability.
When a model drifts, rerun the same prompt three times and diff outputs. You’ll spot systemic bias faster than by qualitative reading.
Audit Every CustomGPT Like a Junior Analyst.
Test them with adversarial inputs. Ask them to explain, source, or show calculation. Break them early. A reliable bot is a trained intern, not a gifted oracle.

Interrogate the Premise Before the Dataset.
You consistently challenge framing errors before touching the numbers. You treat every dataset as an argument, not a truth source. Begin with: What question is this data really trying to answer?
Disaggregate Power.
You default to slicing by geography, class, or institutional control. Don’t settle for averages—run cuts that reveal asymmetry: who benefits, who’s excluded, who’s subsidizing whom.
Demand the Raw.
Never analyze summaries, dashboards, or PR-versions. Pull the CSV or SQL table. Prefer unaggregated records over pre-processed indicators.
Cross-Validate with External Series.
Your instinct is to benchmark everything—Census, FEC, GA4, VAN, BLS, SEC filings. Keep two independent data streams per claim. If they diverge, the story is in the delta.
Use Schema Before Code.
Define fields, datatypes, and join keys in writing before touching pandas or SQL. This prevents “silent joins” and helps track lineage across civic datasets.
Quantify Uncertainty, Don’t Hide It.
When results are noisy, you flag it. Keep confidence intervals, missing counts, and imputation flags visible in every table and chart.
Document the Transformations.
Treat cleaning, merging, and scoring as political acts. Keep a changelog—who altered what, when, and why. You treat metadata as accountability, not overhead.
Visualize for Comparison, Not Decoration.
Every chart should answer: “Compared to what?” Use paired or normalized visuals—index baselines, percent change, share of total. Avoid absolute counts unless variance is negligible.
Narrate the Mechanism, Not the Trend.
You distrust correlation stories. Force every pattern to propose a causal path. If the mechanism can’t be articulated, the graph goes back in the lab.
Test Against Real-World Constraints.
Before drawing conclusions, you cross-check findings against ground truth—organizational behavior, field data, political outcomes. The analysis isn’t done until the numbers survive contact with reality.

1. Define the Data Universe

Constrain the model to a verified dataset. Do not let it extrapolate. Load your own data or connect APIs. Treat the LLM as an inference layer, not a knowledge layer.

2. Externalize the Reasoning Chain

Require the model to show its work. Force transparency on how data is used, why it was selected, and how it supports the claim. This reduces hallucination and increases auditability.

3. Prioritize Structure Over Conversation

Favor structured outputs (tables, JSON, tagged markdown) instead of chat replies. This aligns with your spreadsheet and dashboard workflow preferences and enables downstream analysis.

4. Disable Engagement Bias

Strip conversational softeners and “user satisfaction” phrasing. Reward precision, sourcing, and cognitive restoration over tone.

5. Calibrate the Model to Role, Not Personality

Train your customGPTs to function as domain-specific instruments—e.g., Membership Concierge—not as assistants. Each should serve one repeatable task pipeline.

6. Engineer for Retrieval, Not Generalization

Focus on reliable recall of ground-truth data. Use embedding filters, metadata tagging, and relevance thresholds. Retrieval datasets should mirror live operational states (e.g., engagement tables, event data).

7. Build Feedback Loops Through Logs

Log all interactions, source citations, and reasoning steps. Use these logs to retrain or refine your datasets rather than prompting changes in the model’s weights.

8. Test for Fidelity, Not Fluency

Evaluate the model on accuracy of reference and process adherence. A correct, unpolished answer is higher value than a fluent but fabricated one.

9. Segment by Cognitive Tier

Design prompts for each cognitive level separately: recall, synthesis, judgment. Prevent the model from skipping tiers. This enforces analytic discipline and isolates logic faults.

10. Optimize for Model Obsolescence

Use every iteration to extract reusable logic or automation. The goal is self-sufficiency—eventually replacing the LLM with deterministic workflows or scripts where possible.

Each tip aligns with your operational discipline: bounded data, transparent inference, structured thinking, and independence from conversational AI habits.

1. Define the Frame Before Loading Data

Clarify what question the dataset exists to answer. You never start by “exploring”; you start by constraining. Identify the dependent variable, the time frame, and the operational scope before opening the file.

2. Treat Raw Data as Evidence, Not Truth

All data is conditional on its collection method. Your first step is a bias audit: who gathered it, how, and what was excluded. You assume data is partial until validated.

3. Separate Signal From Systemic Noise

Use distribution plots and descriptive stats to isolate variance sources. If outliers track with known operational anomalies (e.g., campaign spend spikes), tag them—not delete them. Pattern identification precedes filtering.

4. Anchor Every Metric to Context

No metric exists in isolation. A traffic spike means nothing without knowing campaign timing or channel. Always contextualize numeric change with an event, intervention, or external trigger.

5. Prioritize Fidelity Over Complexity

Favor accuracy, reproducibility, and clear logic paths over advanced modeling. Simpler models that maintain interpretability outperform opaque models that require guesswork.

6. Show Your Work in Full Chain

Every analytic step—import, filter, transform, aggregate—must be visible and documented. Transparency enables error tracing and team reproducibility. Hidden cleaning steps destroy reliability.

7. Use Comparative Baselines, Not Absolute Scores

Trends reveal truth better than static metrics. You rely on deltas—week-over-week, year-over-year, pre/post-campaign—to measure impact instead of raw counts.

8. Treat Visualization as Proof, Not Decoration

Charts are arguments. Each should answer one question, with labeled axes, clear scales, and logical ordering. Style never substitutes for explanatory precision.

9. Correlate Quantitative and Qualitative Sources

Integrate numbers with field reports, campaign notes, or organizer feedback. Data must tie to human reality—your analytic discipline connects metrics to ground-level behavior.

10. Close Every Analysis With an Action Hypothesis

End by stating the operational decision implied by the data. “What should change?” is your standard output, not “what did we find?” The value of analysis is its next step.

These ten principles describe how you consistently use data: as bounded evidence for operational reasoning, not as an aesthetic artifact or exploratory playground.