CRAFT™️ Experiment: ChatGPT 04-mini-high: Rethinking A.I. Variables (2 of 5)

The CRAFT™️ Experiment

May 12

Written By Richard Ketelsen

THE CRAFT™️ EXPERIMENT :: WEEK 4 :: POST 2

ChatGPT 04-mini-high: Rethinking A.I. Variables

Please provide a detailed explanation of the benefits of the new data types used in CRAFT. Last week, we worked on new data types and the results are contained in the attached files. Please generate a recap of these results for my blog that explains the new data types in CRAFT in detail.
def create_photo(data):
#H->AI::CreateImage: (Create a photographic image using the data provided)
#H->AI::OnError: (If image creation fails, respond with "Sorry, I cannot make images.")
# Additional implementation details would follow here
URL, ColorPalette, DomainName, WhiteListedDomainName, BlackListedDomainName, Email, FileName, FileHash, MLACitation, MLAInTextCitation, Prompt, PromptLibrary, ConversationHistory, AIRecipe, AIRecipeLibrary, AIActionTemplate, AIWorkflow, AIModelConfig, UserIntent, AISentiment, RichContent, MediaReference, TLDRSummary, UserProfile, Metric, TimeSeries, DataValidator, Transformation, AIInstruction, Comparison, SocialPost, HowTo, Definition, AITone, TargetAudience, WordLength, UserIntent, AISentiment, AIPersona, CommunicationChannel, ChartSpec, CodeSnippet, APIRequest, APIResponse, BusinessLogic, ComplianceRule, ConditionLogic, Checklist, Form, MultipleChoice, Workflow, AIDecisionPoint, KnowledgeGraphNode, DefinedRelationship, ConstraintSet, OutputSchema, ComparisonMatrix, StructuredArgument, RevisionRequest, VersionTag
PROMPT:
Please read the attached project files.
# 01 - INITIAL PROMPT
PROMPT: "Hi, we will work together to brainstorm some content for my website. To begin, I will provide a 10-part prompt that will describe the following:
1. Personal Introduction: Who I am.
2. Content/Website Purpose: The website for which the content will be created and its intended purpose. This provides context for the chat.
3. About the Audience and Website : Target Audience, Pain Points & Unique Value Proposition, Competitive Edge and Elevator Pitch.
4. Content Goals: The objectives to achieve with the content.
5. Role of the A.I.: The expected role that you , the A.I. chat service, should assume in this process.
6. Content Source: The source from which the content will be derived.
7. Inspirations: Inspirations to guide you , the A.I. chat service, in understanding the type of content to create.
8. Constraints: Any specific constraints or aspects to avoid in the content creation process.
9. Ideas for visuals: Any ideas that I have for visuals that may be included in this content. Please do not create these visuals unless specifically asked to do so.
10. Related Stories/Content: If this content is part of a series, this will list and briefly explain the series and list any helpful URLS.
1) Personal Background and Site Overview - Based in Minneapolis, MN, USA, I am a professional in Computer Science and Graphic Design. I work for a Fortune 100 Company as a Senior Cybersecurity Incident Responder where I work with a team to keep people safe from cybersecurity attacks. In addition to 6 years in cybersecurity, my professional history includes 10 years as an Identity Designer and 10 years as an entrepreneur.
2) Website Content Purpose : the website is located at www.ketelsen.ai and is part of the blog section. Ketelsen.ai is An Ongoing AI prompt crafting Experiment. Ketelsen.ai is where the raw brainstorming and AI prompt engineering happens. Think of it as a lab where you consistently test out new prompt ideas, integrate various AI tools (ChatGPT, Midjourney, etc.), and see what sticks. The website features an Exclusive Prompt Collection with over 1,000 in-depth AI prompts covering real-world problems. These prompts are generated each week by multiple AI services, each with its own strengths. The website's Focus is on Breadth & Innovation. The site showcases new and creative ways to use AI—often pushing boundaries by running repeated “what if” experiments with different AI models. Because the goal is to innovate, Ketelsen.ai’s prompts can be very detailed (and at times, a bit overwhelming for newcomers).
3) About the Website:
Important details about Ketelsen.ai:
Step 1: Target Audience
• Demographics: Ages 25–45, global (English-speaking), professionals or entrepreneurs with moderate to high discretionary income.
• Psychographics:
○ Enthusiastic about AI-driven innovation and efficiency.
○ Enjoy experimenting with new technology but can feel overwhelmed by the sheer volume of AI options.
○ Prefer transparent “behind-the-scenes” exploration of prompt engineering.
• Persona Snapshot:
○ “Alex the AI Trailblazer”: 33-year-old product manager at a startup, craves cutting-edge AI prompts and advanced experimentation, wants to stay ahead with minimal wasted time.
Step 2: Pain Points & Unique Value Proposition
Top 3 Pain Points
1. Overwhelm by AI Options: Too many prompts/models to choose from, causing decision fatigue.
2. Difficulty Crafting Effective Prompts: Need guidance to avoid mistakes and harness AI fully.
3. Time Constraints for Testing: Users want quick, reliable feedback on what works without lengthy trial and error.
UVP (1–2 Sentences)
“Ketelsen.ai delivers a constantly evolving library of advanced AI prompts—over 1,000 and counting—so tech-savvy entrepreneurs can rapidly experiment without wasting time on guesswork. By focusing on real-world testing and transparent experimentation, it offers a more expansive and in-depth resource than other popular prompt-engineering websites.”
Step 3: Competitive Edge
1. Massive Prompt Library (over 1,000 and growing).
2. Real-Time Experimentation: Transparent and ongoing updates.
3. Behind-the-Scenes Insight: Detailed explanations of how and why prompts work.
4. Efficiency-Focused: Built around minimal time investment, demonstrating how to achieve more with less.
5. Constant Innovation: Early adoption of new AI models and features.
Step 4: Elevator Pitch
“At Ketelsen.ai, we empower tech-savvy entrepreneurs and AI enthusiasts to unlock cutting-edge automation with minimal time investment. By offering a massive, ever-growing library of high-impact prompts—backed by real-world experimentation and robust behind-the-scenes insights—we help you stay ahead in AI innovation without sacrificing your schedule. It’s your sandbox for AI exploration.”
4) Content Goals - The AI chat responses should result in real prompts that a real entrepreneur can use to achieve a real task. The task will be explained following this.
5) Role of the A.I. - You are “an expert in creating prompts for ChatGPT, Google GEMINI, Anthropic Claude and other similar Gen AI Tools” . You are creative and can use these Gen AI Tools in creative ways.
6) Content Source - Use any resource that is needed to achieve the objectives.
7) Inspirations for Content - The content style should be fun, entertaining and informative. Since the target audience for this site is entrepreneurs , the style should be similar to content found in popular media created for this market such as Forbes, Fortune, and the Wall Street Journal.
8) Constraints -
- Please do not make up any fake information, if there is no data or actual factual information, please respond with "NOT APPLICABLE"
9) Ideas for visuals - More information to come on this
10) Related Stories/Content - This article is part of the ketelsen.ai series on CRAFT™️ is an acronym for Configurable Reusable AI Framework Technology. CRAFT™️ is an on-going weekly experiment where I will be working together with the world’s most advanced commercially available A.I.s to develop a new way way to communicate with A.I. while adapting the principals of Object Orientated Programming to AI Chats. The goal is deeper, more intuitive and more meaningful interactions with A.I. chat tools.
Before continuing the next set of instructions, please list any items that need clarification. If there are no questions, please respond with ""READY"".
RESPONSE: READY
PROMPT:
Please brainstorm some creative ways that variables can be adapted to the CRAFT framework. Please refer to the attached files for information about the current state of this project. For reference, here are some Benefits of CRAFT Variables:
• Reusability: Use the same value across multiple prompts without retyping it each time.
• Consistency: Ensures that all parts of your conversation or blog content reference the same core information, reducing human error.
• Efficiency: Saves tokens by shortening your instructions, which is critical when working within token limits.
Please start with an outline of the highlights of this topic.

7 Creative Ways Variables Power Your CRAFT Framework

In the fast-evolving world of prompt engineering, variables aren’t just placeholders—they’re the backbone of reusable, reliable, and resource-efficient AI conversations. In this post, we’ll weave together everything we’ve covered so far and share a refined, integrated draft that you can drop into your next CRAFT blog article.

==========

1. Recap: Core Benefits of CRAFT VariablesWhen you introduce variables into your CRAFT workflows, you’re setting yourself up for:• Reusability — Declare once, reference everywhere. No more copy‑paste typos or scattered updates.• Consistency — Keep your conversation’s “single source of truth” in one place, so every part of your prompt always lines up.• Efficiency — Swap out a two‑word variable name for a six‑token phrase, and watch your token count (and costs!) shrink.

Declare once, trust forever, and save every time.

==========

2. Variable Declaration & Naming PatternsA predictable naming system is your roadmap in any CRAFT project. Aim for clarity:

Global constants in ALL_CAPS (e.g., PROJECT_NAME, API_VERSION).

Workflow variables in snake_case (e.g., current_step, user_feedback).

Parameterized builders with verb_noun style (e.g., build_prompt, format_date).

Namespacing (e.g., Marketing.campaign_name vs. Analytics.sales_target) to avoid collisions as your library grows.

==========

3. Creative Variable CategoriesOrganize your variables into labeled “drawers” so you always know where to look:

Context Variables capture session state: current_topic = "Variable Naming Patterns"

Persona Variables set the AI’s role: PERSONA_EDITOR = "You are a copyeditor focused on clarity."

Workflow Variables track progress: ONBOARDING_STEP = 2

Environmental Variables hold external config: API_BASE_URL = "https://api.ketelsen.ai/v1"

==========

4. Advanced Variable FeaturesOnce you master basics, layer on these power‑ups:

Computed Variables — Generate today_date programmatically so you never hand‑type “May 12, 2025.”

Scoped vs. Global — Limit side effects by making only some variables session‑wide.

Fallback & Defaults — Use user_name = user_name or "Guest" to never leave blanks.

Validation-Enhanced — Pair variables with simple checks (e.g., email regex) to catch errors early.

==========

5. Integration with Functions & Objects(Brief Glimpse — Full Deep‑Dive Next Week)

Functions consume variables as parameters, keeping your logic pure:generate_welcome_message(PROJECT_NAME, PERSONA_COACH, today_date)

Objects bundle related variables into dot‑notated containers:ctx = SessionContext(user_name, current_topic, onboarding_step)

Stay tuned for a full article on building pipelines and serializing state with CRAFT objects.

==========

6. Use‑Case ScenariosSee variables in action:

Personalization — Swap out USER_NAME and USER_INTENT for bespoke outreach.

Model‑Agnostic Prompts — Switch AI_MODEL = "ChatGPT-4" to test Gemini or Claude.

A/B Testing — Alternate between VARIANT_A and VARIANT_B for data‑driven tuning.

Multilingual Support — Set LANG_CODE = "es" and generate identical prompts in Spanish.

==========

7. Best Practices & RecommendationsKeep your variables in tip‑top shape:• Descriptive names over cryptic abbreviations (e.g., marketing_budget not mb).• Centralized docs in a PROJECT_VARIABLES block as your project’s legend.• Namespacing to isolate domains (Marketing vs. Analytics).• Regular pruning to ditch unused entries and avoid bloat.• Comments & ordering for clarity: group related settings, annotate formats, and version‑control your variables file.

==========

Next StepsWe’ve stitched together every piece of feedback into this draft. Once you’re happy, we’ll roll it out on the blog and follow up soon with our deep dive on functions, objects, and state serialization in CRAFT. Let us know if anything needs tweaking—otherwise, this draft is ready for its debut!

Outline of Creative CRAFT Variable Adaptations

Recap: Core Benefits of CRAFT Variables
• Reusability: Apply the same value across multiple prompts without retyping it.
• Consistency: Guarantee that every part of your conversation or blog references the same core data.
• Efficiency: Conserve tokens by substituting long instructions with compact variable names .
Variable Declaration & Naming Patterns
• Snake_case constants (e.g., PROJECT_NAME) and uppercase for global settings.
• Parameterized builders (e.g., build_expert_prompt(domain, level, task)) for on-the-fly variables .
• Namespaced variables to avoid collisions (e.g., Analytics.sales_target vs. Marketing.sales_target).
Creative Variable Categories
a. Context Variables – capture session metadata, target audience profiles, or conversation history (e.g., CURRENT_USER_PROFILE).
b. Persona Variables – predefine AI “roles” for different tasks (e.g., PERSONA_EDITOR, PERSONA_MARKETING).
c. Workflow Variables – track multi-step processes (e.g., ONBOARDING_STEP = 3), enabling conditional branching.
d. Environmental Variables – embed external config like API endpoints or feature-flags (e.g., API_BASE_URL).
Advanced Variable Features
• Computed Variables – derive values at runtime (e.g., today_date = Date.now()) for dynamic prompts.
• Scoped vs. Global Variables – limit scope to a single workflow or expose globally across sessions.
• Fallback & Default Values – define user_name = user_name or "Guest" to handle missing inputs.
• Validation-Enhanced Variables – attach simple checks (e.g., DataValidator(type="email").validate(user_email)).
Integration with Functions & Objects
• Combine variables with GLOBAL_FUNCTIONS to auto-generate templated prompts (build_complex_persona).
• Bind variables into higher-level “Chat Objects” (e.g., AIRecipe(name="WelcomeFlow", steps=[…])) for modular reuse.
Use-Case Scenarios
• Personalization: Swap out USER_INTENT or TARGET_AUDIENCE to tailor copy for different segments.
• Model-Agnostic Prompts: Store model names or versions as variables, making it easy to switch between ChatGPT, Gemini, or Claude.
• A/B Testing: Define VARIANT_A and VARIANT_B to rapidly compare prompt variants.
• Multilingual Support: Centralize language codes (lang="en" vs. lang="es") to generate localized outputs.
Best Practices & Recommendations
• Keep variable names descriptive but concise.
• Document every project variable in a dedicated PROJECT_VARIABLES section.
• Leverage namespacing to avoid cross-project collisions.
• Regularly review and prune unused variables to maintain clarity.
Next Steps
• Prototype a handful of these patterns in your CFT-PROJ-0001_BUILDINGCRAFT file.
• Test token savings and consistency gains across a sample multi-session workflow.
• Iterate on naming conventions and scoping rules as the project grows.

Sources

o4-mini-high

You said:

Great outline, please expand item 1 Recap: Core Benefits of CRAFT Variables by exploring this topic in more depth.

ChatGPT said:

Thought for 7 seconds

1. Recap: Core Benefits of CRAFT Variables (Expanded)

When you introduce variables into your CRAFT workflows, you’re really layering in an abstraction that pays dividends across every prompt you write. Think of it like setting up labels on storage bins—you give each bin a name, and thereafter you just refer to the bin instead of digging through boxes every time.

A. Reusability

What it means
You declare a value once—say, PROJECT_NAME = "Ketelsen.ai"—and then drop that variable into any prompt needing your project’s name.
Why it matters
- No copy-and-paste fatigue. You never mistype “Ketelsen.ai” as “Ketselen.ai” or “Katelson.ai.”
- Rapid updates. If the name changes to “Ketelsen Labs,” you change it in one spot, and every prompt reflects the new value instantly.
Everyday analogy
It’s like printing address labels instead of writing your home address by hand on every envelope.

B. Consistency

What it means
Variables guarantee that any piece of shared data—an audience profile, a URL, a date—stays identical throughout a multi-step conversation or document.
Why it matters
- Reduced human error. One source of truth prevents “Version Drift,” where small text variations accumulate over time.
- Clearer audits. When you look back over chat logs or blog drafts, you know exactly where to check for definition changes.
Concrete example
pgsql
CopyEdit
// Without variables “Welcome to Ketelsen.ai, Alex the AI Trailblazer!” … “At Ketelsen.ai, our mission is…” // With variables PROJECT_NAME = “Ketelsen.ai” PERSONA = “Alex the AI Trailblazer” “Welcome to {PROJECT_NAME}, {PERSONA}!” … “At {PROJECT_NAME}, our mission is…”
Even if your persona name evolves (“Alex” → “Alexa”), you update just one line.

C. Efficiency (Token Savings)

What it means
Every time you reference a variable name (often just a few characters), you’re avoiding the repeated cost of the full text it represents. In LLM chats, that can add up meaningfully.
Why it matters
- Lower token counts = lower API costs.
- Longer contexts possible. Save room for more conversation by not wasting tokens on repeated boilerplate.
Quick comparison
ApproachText RepeatedApprox. Tokens Each TimeTotal for 5 UsesHard-coded“Senior Cybersecurity Incident Responder”~6 tokens30 tokensVariableTITLE_INCIDENT_RESPONDER~1–2 tokens5–10 tokens
You save ~20–25 tokens just by switching to a variable for five references. That’s enough to afford an extra paragraph of output!

In practice, these three pillars work together: you declare once, you trust forever, and you save every time.

Summary

Reusability lets you define a value a single time and drop it in everywhere without retyping.
Consistency guarantees that all references stay identical, eliminating drift and typos.
Efficiency slashes token usage, cutting costs and freeing up context space for richer interactions.

With these benefits in place, your CRAFT prompts become sturdier, more scalable, and far more maintainable—just like any well-engineered software project.

You said:

Great, please do the same with 2. Variable Declaration & Naming Patterns

ChatGPT said:

Thought for 7 seconds

2. Variable Declaration & Naming Patterns (Expanded)

Establishing clear, predictable naming and declaration practices is the foundation of any maintainable CRAFT workflow. With good patterns in place, both humans and A.I. agents instantly “get” what each variable represents—and where to find or update it.

A. Naming Conventions

Global Constants
- Style: ALL_CAPS_WITH_UNDERSCORES
- Use case: Values that never change during a session (e.g., PROJECT_NAME, API_VERSION).
- Why: Visually stands out and signals “don’t overwrite me.”
Local / Workflow Variables
- Style: snake_case (lowercase with underscores)
- Use case: Temporary values within a multi-step prompt (e.g., current_step, user_feedback).
- Why: Pythonic convention that’s concise and familiar.
Parameterized Variables / Builders
- Style: verb_noun or build_noun (e.g., build_prompt, format_date)
- Use case: Functions or templates that produce a value at runtime.
- Why: The verb cue tells you it’s a “factory” rather than static data.
Namespacing
- Style: Namespace.VariableName (e.g., Marketing.campaign_name, Analytics.sales_target)
- Use case: Group related variables to avoid collisions when projects grow.
- Why: Like folders in a filing cabinet—keeps categories tidy.

B. Declaration Patterns

Top-of-Prompt Block
makefile
CopyEdit
// Variable declarations PROJECT_NAME = "Ketelsen.ai" DEFAULT_LANGUAGE = "en" user_name = "Guest" onboarding_step = 1
- Why: Centralizes all definitions so you can glance at them before reading the logic below.
Inline Overrides
cpp
CopyEdit
// Later in the conversation onboarding_step = onboarding_step + 1 // Advance the workflow
- Why: Makes it clear when and where a value changes.

C. Parameterized Builders

Pattern:
python
CopyEdit
def build_persona(name, role, tone="casual"): return f"Persona(name={name}, role={role}, tone={tone})"
Benefit: Generate rich, structured variables on the fly—no hard-coding dozens of permutations.
Analogy: Like a latte machine knob: choose your “strength” and “milk type” and get a fresh cup every time.

D. Namespacing for Scale

Example Structure:
kotlin
CopyEdit
class Marketing: campaign_name = "SpringLaunch" budget = 50000 class Analytics: sales_target = 120000 region = "EMEA"
Why it matters: When you have dozens of variables, namespacing prevents “cross-talk” and makes auto-completion in your editor (or query) far more useful.
Everyday Analogy: Folder hierarchies on your computer—“Work/Finance/Q1” vs. “Personal/Finance/Q1.”

E. Quick Reference Table

Variable TypeNaming StyleExamplePurposeGlobal ConstantALL_CAPS_WITH_UNDERSCORESAPI_VERSION = "v2.1"Never-changing session or project settingsLocal / Workflowsnake_casecurrent_step = 2Track state within a multi-step promptParameterized Builderverb_noun / build_nounbuild_prompt("sales", "draft")Create templated values dynamicallyNamespaced ConfigurationNamespace.VariableNameAnalytics.sales_targetGroup related variables for clarity

Everyday Analogy:

Think of your CRAFT variables like labels on spice jars in a kitchen.
– Globals (salt, sugar) live front and center, always the same.
– Workflow variables (today’s special blend) sit on a shelf you update each day.
– Builders (mixed spice grinder) let you combine ingredients on demand.
– Namespaces (baking vs. cooking spices) keep the right flavors in the right place.

Summary

By adopting clear declaration blocks, consistent naming conventions, parameterized builders, and thoughtful namespacing, your CRAFT variables become self-documenting, scalable, and a joy for both humans and A.I. agents to work with.

You said:

Great, please do the same with 3. Creative Variable Categories

ChatGPT said:

3. Creative Variable Categories (Expanded)

When you move beyond one-off values and start grouping variables by purpose, you unlock powerful ways to organize and drive your CRAFT workflows. Think of these categories as labeled drawers in your “prompt toolbox,” each holding a distinct kind of resource.

A. Context Variables

What they are
Snapshots of the conversation’s state or environment—everything the AI needs to “know” before generating its next response.
Why they matter
- Maintain continuity: Carry over details like the current topic, user preferences, or prior answers without restating them.
- Adaptive behavior: Let the AI adjust its tone or depth based on where you are in the flow.
Examples
ini
CopyEdit
current_topic = "Variable Naming Patterns" user_expertise = "intermediate" session_start_time = "2025-05-12T14:30:00Z"
Everyday analogy
Like a sticky note on your whiteboard that says “Meeting in Progress: Brainstorming,” so anyone (or any AI) stepping in knows exactly what’s happening.

B. Persona Variables

What they are
Predefined “roles” or character settings that shape the AI’s voice, perspective, and expertise level.
Why they matter
- Consistent voice: Ensure that when you ask for “Expert Reviewer” vs. “Friendly Coach,” you get distinctly different styles.
- Rapid switching: Flip personas mid-conversation without rewriting the entire prompt.
Examples
ini
CopyEdit
PERSONA_EDITOR = "You are a meticulous copyeditor focused on clarity and grammar." PERSONA_STRATEGIST= "You are a high-level marketer who thinks in big ideas and KPIs."
Everyday analogy
Like slipping on different hats—one day you wear the “chef’s toque,” the next the “artist’s beret,” and your behavior changes accordingly.

C. Workflow Variables

What they are
Markers that track where you are in a multi-step sequence or conditional process.
Why they matter
- Branching logic: Jump to the right next step (e.g., follow-up question vs. final summary).
- Stateful prompts: Let the AI know, “We’re now on step 3 of 5,” so it can tailor its output.
Examples
ini
CopyEdit
ONBOARDING_STEP = 2 // 1=Intro, 2=Collect Data, 3=Generate Plan needs_review = True // Flag whether the draft needs human approval
Everyday analogy
Like the progress bar on a checkout page—“2 of 4 steps complete”—guiding you smoothly to the finish line.

D. Environmental Variables

What they are
External or configuration values that inform the AI about settings outside the chat itself.
Why they matter
- Dynamic integration: Plug in API endpoints, feature flags, or deployment modes without rewriting prompts.
- Separation of concerns: Keep code/config data isolated from prompt logic.
Examples
ini
CopyEdit
API_BASE_URL = "https://api.ketelsen.ai/v1" FEATURE_X_ENABLED = False
Everyday analogy
Like the thermostat setting in a smart home—“Eco mode” vs. “Comfort mode”—which changes behavior across all your devices at once.

Summary of Creative Categories

Context Variables: Capture where you are and what matters right now.
Persona Variables: Define who—or what voice—the AI should adopt.
Workflow Variables: Track progress and enable conditional branching.
Environmental Variables: Encapsulate external configs and feature toggles.

By thoughtfully categorizing your variables, you create a modular, scalable framework that’s easy to navigate—and even easier to extend as your CRAFT project grows.

o4-mini-high

You said:

Going well, proceed with 4. Advanced Variable Features

ChatGPT said:

Thought for 14 seconds

4. Advanced Variable Features (Expanded)

When you graduate from basic variables to advanced features, you give your CRAFT workflows superpowers—dynamic behavior, error-resistance, and fine-grained control. Below are four key techniques to elevate your variable toolkit.

A. Computed Variables

What they are
Variables whose values are calculated at runtime rather than hard-coded up front.
Why they matter
- Fresh context: Always reflect the current date, time, or any real-time metric without manual updates.
- Dynamic prompts: Tailor your questions based on yesterday’s results or today’s weather for more relevant interactions.
Example
python
CopyEdit
# Compute a human-friendly date label today_date = datetime.utcnow().strftime("%B %d, %Y") days_until_event = (event_date - datetime.utcnow()).days
You can then write:
“Today’s date is {today_date}, and we’re {days_until_event} days away from the product launch.”
Everyday analogy
Like the dashboard in your car that auto-updates your speed and fuel level—no need to guess.

B. Scoped vs. Global Variables

What they are
- Global: Accessible anywhere in your CRAFT session or across multiple prompts.
- Scoped (Local): Limited to a specific function, step, or workflow segment.
Why they matter
- Prevent collisions: Avoid accidentally overwriting a value when you only meant to change it for one mini-task.
- Clear intent: Readers (and A.I.) instantly know whether a variable is “universal” or “just for this section.”
Example
python
CopyEdit
# Global constant PROJECT_NAME = "Ketelsen.ai" def onboarding_step(step): # Scoped variable local_message = f"Step {step} of onboarding" return local_message
Everyday analogy
Think of your smartphone apps: your contact list (global) is always there, while the text you type in one chat window (scoped) doesn’t carry over to another.

C. Fallback & Default Values

What they are
Safety-net assignments that ensure a variable always has a usable value, even if input is missing or malformed.
Why they matter
- Robustness: Prevents your prompts from crashing or producing “None” or blank outputs.
- Graceful degradation: You can still proceed with a sensible default rather than stopping entirely.
Example
python
CopyEdit
# Use “Guest” if user_name wasn’t provided user_name = user_name if user_name else "Guest" # Or more succinctly user_name = user_name or "Guest"
Now every greeting falls back to:
“Hello, Guest—welcome aboard!”
Everyday analogy
Like keeping a spare tire in your trunk: if you get a flat, you don’t end up stranded—you keep rolling.

D. Validation-Enhanced Variables

What they are
Variables coupled with simple checks or validators to ensure they meet expected formats or ranges before use.
Why they matter
- Early error catching: Flag typos, invalid emails, or out-of-range numbers before they pollute your output.
- Cleaner logic: Your main prompts stay focused on content, not on handling every edge case.
Example
python
CopyEdit
# Simple email validator def is_valid_email(email): return re.match(r"[^@]+@[^@]+\.[^@]+", email) user_email = input_email if not is_valid_email(user_email): user_email = "no-reply@ketelsen.ai" # fallback
Then you can confidently write:
“We’ll send the report to {user_email}.”
Everyday analogy
Like a bouncer checking IDs at the door—only valid guests get in, and the process is swift.

Summary

By layering in these advanced features, your variables become:

Computed for real-time relevance
Scoped to avoid accidental overwrites
Protected with defaults so nothing ever breaks
Validated to catch errors early

Together, they transform simple placeholders into a resilient, adaptive backbone for your CRAFT prompts—empowering both you and the A.I. to focus on creativity, not boilerplate.

o4-mini-high

You said:

Please expand 5.Integration with Functions & Objects. Note - this will be a topic in a follow-up blog article in the next weeks so some basic glimpse is fine and more details will follow later. Focus on how the variables fit into the bigger picture

ChatGPT said:

Thought for 8 seconds

5. Integration with Functions & Objects (Glance into the Bigger Picture)

In the coming weeks, we’ll dive deep into how CRAFT variables mesh with richer function libraries and object-oriented patterns. For now, here’s a high-level peek at how variables become the glue that holds those abstractions together.

A. Variables as Function Inputs

Concept
Treat your predeclared variables as the parameters to reusable functions, so you never hard-code values inside your logic.
How it fits
python
CopyEdit
def generate_welcome_message(project, persona, date): return f"Welcome to {project}! I’m {persona}, here on {date} to guide you." # Later in your prompt flow welcome = generate_welcome_message(PROJECT_NAME, PERSONA_COACH, today_date)
Here, PROJECT_NAME, PERSONA_COACH, and today_date are simple variables—but they feed directly into your function, keeping the function pure and your data definitions centralized.

B. Objects as Variable Containers

Concept
Bundle related variables into small objects or data classes, giving you dot-notation access and a clear namespace.
How it fits
python
CopyEdit
class SessionContext: def __init__(self, user, topic, step): self.user = user self.topic = topic self.step = step ctx = SessionContext(user_name, current_topic, onboarding_step)
Now, anywhere in your prompts you can reference ctx.user, ctx.topic, or ctx.step, rather than juggling separate standalone variables.

C. Chaining Functions & Objects

Concept
Build small, single-purpose functions that accept and return objects, so you can compose complex flows without losing track of your state.
How it fits
python
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def advance_step(context): context.step += 1 return context # Chain steps in a pipeline ctx = SessionContext("Alice", "Landing Page Review", 1) ctx = advance_step(ctx) # Now step == 2 ctx = advance_step(ctx) # Now step == 3
Because ctx is an object holding all relevant variables, each function can update and carry forward the full state.

D. Preview of What’s Next

Utility Classes
Soon we’ll look at small helper classes (e.g., PromptBuilder, Validator) that internally rely on CRAFT variables for configuration.
Higher-Order Functions
We’ll explore functions that generate other functions—templates for “make-your-own” prompt builders that insert your project’s variables automatically.
Serialization & Persistence
Next time, we’ll see how to serialize these objects (with their embedded variables) into JSON or YAML, so you can save/load entire chat states.

Everyday Analogy

Imagine your variables are individual LEGO bricks. Functions are the little instruction sheets that tell you how to snap bricks together into sub-assemblies. Objects are the special baseplates that hold groups of bricks—you pick up or move the entire section at once, rather than fiddling with each brick.

Summary

Functions consume your variables as inputs and produce clean outputs—keeping logic and data definitions separate.
Objects package related variables into coherent, dot-accessible structures, making state easy to pass around.
Chaining these together sets the stage for powerful, modular pipelines that you’ll build out in the next article.

o4-mini-high

You said:

Please expand on 6. Use-Case Scenarios

ChatGPT said:

6. Use-Case Scenarios (Expanded)

Variables shine brightest when applied to real-world workflows. Below are four common scenarios where CRAFT variables supercharge your AI-driven processes.

A. Personalization

What it is
Dynamically swapping out key details—like user names, goals, or product features—so each prompt feels tailor-made.
Why it matters
- Higher engagement: Personalized messages grab attention more effectively than one-size-fits-all text.
- Scalability: Need to send 100 outreach emails? Change one variable and let the AI generate 100 bespoke messages.
Example
ini
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USER_NAME = "Jordan" USER_INTENT = "improving email open rates" PROMPT = f"Hi {USER_NAME}, I see you're {USER_INTENT}. Here’s a 3-step plan to help you achieve that…"
Everyday analogy
Like printing mailing labels with each recipient’s name instead of handwriting “Dear Friend”—it feels personal and saves time.

B. Model-Agnostic Prompts

What it is
Abstracting the AI engine (ChatGPT, Gemini, Claude, etc.) behind a variable so you can switch back-ends without rewriting prompts.
Why it matters
- Future-proofing: If a new model outperforms your current choice, you simply update AI_MODEL instead of hunting through every prompt.
- A/B comparisons: Test how different engines handle the same prompt to pick the best output.
Example
ini
CopyEdit
AI_MODEL = "ChatGPT-4" # or "Gemini-Pro", "Claude-2" PROMPT = f"Using {AI_MODEL}, summarize this report in bullet points."
Everyday analogy
Like ordering coffee at a café chain: you specify “skinny latte” once, then choose whether it’s made on the traditional espresso machine or their new super-automatic brewer—same order, different hardware.

C. A/B Testing

What it is
Defining multiple prompt variants via variables (e.g., VARIANT_A, VARIANT_B) and automatically alternating between them to see which yields better results.
Why it matters
- Data-driven refinement: Rather than guessing which prompt phrasing works best, you gather real performance data.
- Iterative improvement: Quickly retire underperforming variants and double down on winners.
Example
ini
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VARIANT_A = "Explain the new feature in three simple steps." VARIANT_B = "Describe the new feature with an analogy." SELECTED = VARIANT_A if campaign_day % 2 == 0 else VARIANT_B
Everyday analogy
Like running two headlines in an email subject line test—you see which one gets more clicks and then send the winner to the rest of your list.

D. Multilingual Support

What it is
Centralizing language settings (e.g., LANG = "en" or "es") so the same prompt logic can spin out outputs in multiple languages.
Why it matters
- Global reach: One codebase serves diverse audiences without duplicating entire prompt libraries.
- Consistent style: Ensures identical structure and quality across translations.
Example
ini
CopyEdit
LANG_CODE = "es" TRANSLATE_PROMPT = f"Please translate the following into {LANG_CODE}: '{BASE_TEXT}'"
Everyday analogy
Like setting the language on your phone: menus, apps, and notifications immediately switch to your chosen locale, using the same underlying software.

Everyday Takeaway

Personalization makes each interaction feel handcrafted.
Model-agnostic prompts keep you flexible and future-ready.
A/B testing turns guesswork into measurable insights.
Multilingual support breaks down language barriers with minimal extra effort.

Summary

CRAFT variables empower you to:

Customize content for any individual or segment.
Swap AI engines at will, without rewriting.
Experiment systematically to find top-performing prompts.
Translate effortlessly for a global audience.

By weaving these scenarios into your workflows, you build a prompt-engineering system that’s simultaneously powerful, adaptable, and easy to maintain.

o4-mini-high

You said:

Please expand on 7. Best Practices & Recommendations

ChatGPT said:

Thought for 7 seconds

7. Best Practices & Recommendations (Expanded)

Even the most powerful variable system can become a tangled mess without some guardrails. Here are tried-and-true practices to keep your CRAFT variables clean, clear, and effective.

A. Choose Descriptive—but Concise—Names

Why it matters:
- Clarity for you and the AI: A name like user_preference_theme immediately signals its role, whereas something cryptic like upt forces extra mental mapping.
- Avoid ambiguity: Descriptive names reduce “What does this do?” questions later.
Guidelines:
- Aim for 2–4 words max.
- Use full words over obscure abbreviations (e.g., marketing_budget not mktg_bud).
- Prefix booleans with is_ or has_ (e.g., is_premium_user).
Everyday analogy:
Like labeling your pantry jars “Almond Flour” instead of “AF”—you always know what you’re grabbing.

B. Centralize Documentation in a PROJECT_VARIABLES Section

Why it matters:
- Single source of truth: Rather than hunting through 10 different prompts, you know all your variables live in one block.
- Onboarding ease: New collaborators (or future you) can get up to speed by scanning that one section.
What to include:
yaml
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PROJECT_VARIABLES: PROJECT_NAME: "Ketelsen.ai" # Your site’s brand identifier DEFAULT_LANGUAGE: "en" # ISO code for output localization ONBOARDING_STEP: 1 # Tracks where we are in the flow ...
Everyday analogy:
Like a legend on a map—once you learn it, every symbol makes sense.

C. Leverage Namespacing to Avoid Collisions

Why it matters:
- Scalability: When your prompt library grows, flat names like start_date in two different contexts can accidentally overwrite each other.
- Modularity: You can move or share namespaces (e.g., Marketing vs. Analytics) without merging every variable.
Pattern:
python
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class Marketing: campaign_name = "SpringSale" budget = 100000 class Analytics: campaign_name = "Q2Review" # Different “campaign_name” in another namespace report_format = "PDF"
Everyday analogy:
Like having separate drawers for “Office Supplies” and “Kitchen Utensils”—you never mix paperclips with spoons.

D. Schedule Regular Reviews & Prune Unused Variables

Why it matters:
- Avoid “variable bloat”: Old or orphaned variables clutter your workspace and can lead to confusion.
- Maintain performance: Fewer variables mean less cognitive load and fewer tokens wasted.
How to do it:
- Monthly check-ins: Skim your PROJECT_VARIABLES and highlight any names you haven’t used in recent prompts.
- Automated linting (optional): If you build tooling later, flag declarations that never get referenced.
Everyday analogy:
Like spring-cleaning your closet—if you haven’t worn it in a year, it’s probably safe to let it go.

E. Extra Tips for Long-Term Health

Annotate with Comments
- Briefly note units or expected formats (# in USD, # ISO date YYYY-MM-DD).
Enforce Consistent Ordering
- Group by category: constants → context → workflow → personas.
Version-Control Your Variables File
- Track when names change or defaults shift so you can roll back if something breaks.
Use Defaults & Fallbacks Wisely
- Whenever possible, set sane defaults so missing inputs don’t derail the flow.

Summary

Descriptive names keep everything transparent.
Centralized docs act as your project’s legend.
Namespacing prevents cross-talk in large libraries.
Regular pruning stops bloat in its tracks.
Bonus tips around comments, ordering, and versioning round out a healthy workflow.

By baking these practices into your routine, your CRAFT variables will stay as nimble and reliable as the code they support—ready to scale as your projects grow.

CRAFT™️The CRAFT™️ Experimentweek4