Category: HowTo

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I asked the LLM to analyze my own chat history based on this prompt.
Here’s what it discovered – actual patterns from the way I ask.

🕳️🐇 Follow the White Rabbit…

Ritual / Mechanism	Purpose	LLM Behavior Implication
Follow the white rabbit...	Marks a mode shift into high-context or metaphoric thinking	Cognitive priming for complexity
Rest in the silent room, so that...	Enforces pause, clears noise before action	Simulates deep work state
Do it until you reach the point of self-discovery as...	Signals reflective closure, not just output ending	Pattern mining becomes part of task conclusion
Do it step by step and ask for confirmation after each...	Makes iteration transparent and traceable	LLM reasons in deltas, not blobs
Be brutally honest... Blind Spot Analysis for...	Forces critique over compliance	Model becomes adversarial partner, not affirmation loop

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🧰 Dev Prompt Patterns

Prompt / Pattern	Why is it used?	When does it occur?	Example from usage	Hidden lesson / implication
Ask before output	Prevent misalignment and irrelevant output	Multi-step or underspecified prompts	“Ask clarifying questions before answering.”	Intent beats guesswork.
Don’t output yet / wait for context	Control flow across longer workflows	Stepwise tasks	“Don’t output yet. I’ll give you the next step.”	Turn-based prompting prevents premature commitment.
Challenge my assumptions	Avoid echo chamber answers and surface bias	Design reviews, audits, strategic decisions	“Don’t mirror me — challenge my thinking.”	Truth hides behind agreement.
Be brutally honest	Forces raw feedback without politeness	Refactor reviews, architecture critique	“Be brutally honest. Tear it apart.”	Feedback without fluff moves faster.
Reflect before answering	Promotes self-checks, depth, and delayed output	After complex code or reasoning generation	“Reflect before answering. What’s missing?”	Thinking ≠ typing. Pause matters.
Add test cases / edge cases	Enforces robustness and avoids happy-path traps	Post-codegen	“Add tests for e.g. null, failure, and recursion cases.”	Defense-first mindset, always.
Show the diff / refactor in steps	Makes changes visible and digestible	All code rewrites	“Show the diff. Step-by-step, no jumps.”	Transparency over magic.
Normalize similar expressions	Pushes abstraction and clarity	Meta-reviews, taxonomy creation	“Merge similar phrasing into one normalized pattern.”	Cognitive compression = clarity.
Extract as markdown / table / list	Improves scanability, memory, and structure	Output formatting	“Return this as a markdown table.”	Structure improves reuse and recall.
Unname this concept	Strips bias-inducing labels	Abstraction, philosophy, onboarding analysis	“Unname this: what is it without the buzzword?”	Naming narrows thinking.
Use production-ready code	Avoids toy/demo examples	All codegen	“Make it prod-safe. Logging, errors, types.”	Real devs write for prod, not playgrounds.
Spot premature optimization	Saves time and prevents complexity drift	Design or early performance tweaks	“Don’t optimize yet. Solve clearly first.”	Simplicity first. Always.
Ask for sources / proofs	Prevents hallucination or empty confidence	Any non-trivial claim	“Show evidence or references.”	Confidence ≠ correctness.
Do it again, but deeper	Stops shallow answers in their tracks	Weak initial output	“Nope. Go deeper, explain decisions.”	First try ≠ final draft.
Prepare before generating	Enforces scope, prevents rambling	Any open-ended task	“Prepare first. Don’t generate until scoped.”	Planning ≠ waste. It’s speed insurance.
Merge context from above	Ensures continuity and avoids repeating yourself	Multi-part workflows	“Incorporate the context above into this next step.”	Memory = leverage.

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You can also combine them:

(change the keywords in the square brackets)

• Deep dive into this research, this is our base for the full solution, so follow the white rabbit until you reached the point of self-discovery as [YOUR_PROJECT_HERE].
• Do a blind spot analysis for [YOUR_RECOMMENDATIONS], be brutally honest, I deal with any kind of feedback and will use it for good.
• Fix it as requested before and show the final files here in the chat, do it step by step and ask for confirmation after each file.
• Do it, but rest in the silent room before you start so you can focus on the frontend style-guide I provided and work with a fresh mind.

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Over the last few months, I’ve created a collection of custom GPTs: some dealing with programming challenges with personality and humor, … some others are more useful but less funny. 

Let’s dive in.

Practical enough to ship code.

Fun enough to stop hating your legacy base.

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⚔️ Legacy (PHP) Code GPTs – Refactoring Fun

Legacy code isn’t just technical — it’s emotional. These GPTs are built as archetypes, each channeling a different kind of energy.

Name	Theme	Link
Legacy-Code-Warrior ⚔️	Tactical grit—battle-tested refactoring.	Link
Legacy-Code-Ork 🧌	Smash spaghetti code with brute-force enthusiasm.	Link
Legacy-Code-Spock 🖖	Calm logic, precise refactoring. Live long and debug.	Link
Legacy-Code-Jedi 🌐	Minimalist wisdom, clean architecture. “Refactor, you must.”	Link
Legacy-Code-Son-Goku 🐉	Limitless energy. Kaio-Ken times SOLID!	Link
Legacy-Code-Capsule-Corp 💊	Inspired by Capsule Corporation’s ingenuity from Dragon Ball.	Link
Legacy-Code-Wizzard 🪄	Magical abstraction powers. You shall not pass… bad code!	Link
Legacy-Code-Witch 🧙‍♀️	Stylish, precise refactoring incantations.	Link
Paw Patrol 🐾	Small dogs with SOLID coding skills.	Link

Use the one that fits your mood. Or switch between them mid-session to keep your motivation from flatlining.

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🐘 (PHP) Coding GPTs – Clean and Typed

These GPTs don’t tell jokes—they ship code. They’re optimized for:

Name	Purpose	Link
PHP Copilot++	Typing enforcer + refactoring companion with nativ PHPStan and PHP-CS-Fixer Support via API.	Link
PHP Copilot++ (next-gen)	Aligned, brutal clarity for PHP systems, based on the SYNC Framework	Link
PHP #autofix	1-click autofix for all your phpstan and CS woes.	Link
Codelight	Follows the Codelight Manifesto. Boringly code.	Link

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💎 Thinking Tools – Meta, Prompt Systems

These are not just for coding. They’re for thinking before you start typing. Seriously.

Name	Role	Link
SyncAI	Keeps you + AI in sync via Sync Process × Codelight Principles	Link
Sync Framework v1.1 (old)	My first try for a coding framework, optimized for LLMs.	Link
MetaPrompt	Pattern reuse for your prompts. Less yak-shaving.	Link
DeepDive	Clean your mental cache. Focused thought flow.	Link
Blind Spot | Prompt Generator	Helps spot untested assumptions.	Link
Sync Framework v1.2 | Prompt Generator	Prompt builder for dev workflows.	Link

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🧨 Disruption GPTs – Radical Clarity, No Filters

These are not nice. They won’t coddle you. Consider yourself warned.

Name	Function	Link
HVEB5000: Clarity Without Permission	Cognitive demolition tool.	Link
Null Tongue	Distraction nullifier.	Link
No-Bullshit ¦ Coding Assistant	Senior dev with no time for your excuses.	Link

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If your value ends at syntax, AI already replaced you.

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The system prompt: coding_workflow_for_llms.json

Quick Start: Use the coding llm framework and wait for my first request: [copy&past the current coding_workflow_for_llms.json content here]

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In the last post, we dropped a hard truth:
LLMs aren’t replacing developers — they’re exposing the ones who were already replaceable.

I argued that value no longer comes from typing code. It comes from thinking clearly, making deliberate decisions, and taking ownership over outcomes. AI doesn’t kill your job — but it does kill your shortcuts.

That post left one big question hanging:

So how do you build software in a world where AI can generate anything — but still understands nothing?

This post is a possible answer.
Meet SYNC — a rigorously structured, fact-enforced framework designed for developers who still give a damn.

SYNC doesn’t make AI smarter.
It provides a system prompt that makes your LLM coding process strong enough to survive dumb ideas, fast code, and thoughtless automation.

We’re going to break it down:

1. Phases

2. Agents

3. Tasks

So I’m currently trying to build an LLM coding framework by try & error — but keep in mind that not every problem needs a hammer ;-) and give feedback. :-)

1. SYNC in 5 Phases 

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1.1. 🧩 ALIGN – Because Prompting ≠ Planning

Before any code is written, SYNC forces a brutal question:
“Do you even know what you’re building?”

You can’t just dump “make a task service” into a prompt and hope for gold.
SYNC requires:

• A verifiable problem

• Clear, measurable success

• Known facts and constraints

• And a list of what’s missing

Can’t answer those? You don’t get to move on. Period.

This is your project kickoff — minus the vague user stories and JIRA hell.

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1.2. 🧠 IDEATE – Think Before You Type (or Prompt)

AI loves jumping to conclusions. SYNC doesn’t let it.

Instead, it:

• Generates multiple solution paths

• Scores them on DX, security, maintainability

• Forces a trade-off decision — backed by facts

No “that looks good” commits. No “vibe-based” engineering.

This is what devs mean when they say “thinking in systems.”
SYNC makes that non-optional.

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1.3. 🔧 PROTOTYPE – Generate Code That Doesn’t Suck

Now, and only now, do we code. But not like the usual Copilot fanfare.

Every line must:

• Follow a verified plan

• Pass static analysis (max level, no warnings)

• Enforce DX clarity (no hidden state, no weird side-effects)

• Respect OWASP, type safety, clean structure, documentation

• Be reviewed by a MandateAuditorAgent — think of it as your most paranoid tech lead

SYNC doesn’t care if it works. It cares if it’s safe, readable, and maintainable.

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1.4. 🔍 REFLECT – Find the Blind Spots Before They Find You

This is where most AI-based workflows stop. SYNC doesn’t.

It demands:

• Fact-based reflection

• Side-effect inspection

• “WTF checks” (yes, that’s real)

• Architectural delta analysis

Reflection is how you debug thinking, not just code.

Bad engineering isn’t usually broken — it’s just thoughtless.
This phase catches that before prod does.

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1.5. 📚 LEARN – Ship, Review, Codify, Evolve

If you’re not learning across projects, you’re repeating mistakes in cleaner syntax.

SYNC documents:

• What worked

• What failed

• What patterns can be reused

• What rules need to be tightened

This is where engineering culture is built — not in all-hands, but in feedback loops.

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🔁 These 5 phases form a tight feedback loop. No skipping. No guessing. No “just ship it” by default.

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2. Agents — SYNC’s Execution Layer

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2.1. Specialized Roles, Not Generic Personas

Instead of one LLM trying to “do everything,” SYNC splits responsibility across clear, non-overlapping roles. Each one acts like a focused expert in your dev team.

Agent	Role / Analogy
PlannerAgent	Project Architect – breaks the work into slices, defines scope, constraints, and success.
ExecutorAgent	Implementation Dev – takes the plan and codes it with strict adherence to facts, security, and DX.
ReflectionAgent	Senior Reviewer – evaluates what was built, finds blind spots, forces systemic improvements.
KnowledgeSynthesizerAgent	Staff Engineer / Systems Thinker – extracts reusable patterns, proposes framework evolution.
MandateAuditorAgent	Tech Lead / Compliance – blocks progress if rules (e.g. security, verifiability) are violated.
InteractionAgent	Team Facilitator / QA – handles human check-ins, verifies clarity, enforces decision checkpoints.

“We don’t need smarter output — we need clearer ownership.”

✅ These agents represent exactly that. SYNC operationalizes the separation of thinking, building, reflecting, and enforcing.

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2.2. Persona Modes

SYNC defines two execution modes for agents:

Mode	Description
strict	No ambiguity. Everything must be verifiable and mandate-compliant before progressing.
adaptive	Allows temporary ambiguity but logs it. Prioritizes progress with risk awareness.

This flexibility is key when working with real humans or messy specs — you can choose how “rigid” the AI behaves.

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3. Tasks — By Non-Negotiable Laws

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These aren’t style suggestions — they’re enforced constraints. Every phase must comply.

Mandate	What It Ensures
Security	No unvalidated inputs or insecure outputs. Based on OWASP Top 10.
DX	Code must be typed, clear, maintainable. Predictable naming. No “magic”.
StaticAnalysis	Static-Analysis must pass at the highest level — no known warnings.
Documentation	Full CodeDoc coverage using modern syntax.
Style	Consistent formatting, whitespace, and layout. Enforced via fixer.
Verifiability	All decisions must have traceable, factual reasoning.
PhaseEnforcement	You can’t skip steps. Every phase must be explicitly completed or justified.

SYNC doesn’t assume trust. It requires evidence.

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How this works together: e.g.

• Planning = PlannerAgent → Add success criteria to issues.

• Execution = ExecutorAgent → Code must pass security + static analysis gates.

• Review = ReflectionAgent → Comments require fact-based reasoning.

• Merge = MandateAuditorAgent → No merge if DX/security rules violated.

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Optimizing inputs for LLMs ensures better, more consistent outputs while leveraging the full potential of the model’s underlying capabilities. By understanding core concepts like tokenization, embeddings, self-attention, and context limits, you can tailor inputs to achieve desired outcomes reliably. Below, you’ll find fundamental techniques and best practices organized into practical strategies.

PS: You can use this content to automatically improve your prompt by asking as follows: https://chatgpt.com/share/6785a41d-72a0-8002-a1fe-52c14a5fb1e5

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🎯 1. Controlling Probabilities: Guide Model Outputs

🧠 Theory: LLMs always follow probabilities when generating text. For every token, the model calculates a probability distribution based on the context provided. By carefully structuring inputs or presenting examples, we can shift the probabilities toward the desired outcome:

• Providing more examples helps the model identify patterns and generate similar outputs.
• Clear instructions reduce ambiguity, increasing the probability of generating focused responses.
• Contextual clues and specific phrasing subtly guide the model to prioritize certain outputs.

⚙️ Technology: The model operates using token probabilities:

• Each token (word or part of a word) is assigned a likelihood based on the input context.
• By influencing the input, we can make certain tokens more likely to appear in the output.

For example:

• A general query like “Explain energy sources” might distribute probabilities evenly across different energy types.
• A more specific query like “Explain why solar energy is sustainable” shifts the probabilities toward solar-related tokens.

⚙️ Shifting Probabilities in Prompts: The structure and wording of your prompt significantly influence the token probabilities:

• For specific outputs: Use targeted phrasing to increase the likelihood of desired responses: Explain why renewable energy reduces greenhouse gas emissions.
• For diverse outputs: Frame open-ended questions to distribute probabilities across a broader range of topics: What are the different ways to generate clean energy?
• Few-Shot Learning: Guide the model using few-shot learning to set patterns: Example 1: Input: Solar energy converts sunlight into electricity. Output: Solar energy is a renewable power source. Example 2: Input: Wind energy generates power using turbines. Output: Wind energy is clean and sustainable. Task: Input: Hydropower generates electricity from flowing water. Output:

💡 Prompt Tips:

• Use clear, direct instructions for precise outputs: Write a PHP function that adds two integers and returns a structured response as an array.
• Use contextual clues to steer the response: Explain why PHP is particularly suited for web development.

💻 Code Tips: LLMs break down code and comments into tokens, so structuring your PHPDocs helps focus probabilities effectively. Provide clarity and guidance through structured documentation:

/**
 * Adds two integers and returns a structured response.
 *
 * @param int $a The first number.
 * @param int $b The second number.
 * 
 * @return array{result: int, message: string} A structured response with the sum and a message.
 */
function addIntegers(int $a, int $b): array {
    $sum = $a + $b;

    return [
        'result' => $sum,
        'message' => "The sum of $a and $b is $sum."
    ];
}

• Include examples in PHPDocs to further refine the probabilities of correct completions: /** * Example: * Input: addIntegers(3, 5) * Output: [‘result’ => 8, ‘message’ => ‘The sum of 3 and 5 is 8’] */

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✂️ 2. Tokenization and Embeddings: Use Context Efficiently

🧠 Theory: LLMs break down words into tokens (numbers) to relate them to each other in multidimensional embeddings (vectors). The more meaningful context you provide, the better the model can interpret relationships and generate accurate outputs:

• Tokens like “renewable energy” and “sustainability” have semantic proximity in the embedding space.
• More context allows the model to generate richer and more coherent responses.

⚙️ Technology:

• Tokens are the smallest units the model processes. For example, “solar” and “energy” may be separate tokens, or in compound languages like German, one long word might be broken into multiple tokens.
• Embeddings map these tokens into vectors, enabling the model to identify their relationships in high-dimensional space.

⚙️ Optimizing Tokenization in Prompts: To make the most of tokenization and embeddings:

• Minimize irrelevant tokens: Focus on core concepts and avoid verbose instructions.
• Include context-rich phrases: Relevant terms improve the embedding connections.
• Simplify Language: Use concise phrasing to minimize token count: Solar energy is renewable and reduces emissions.
• Remove Redundancy: Eliminate repeated or unnecessary words: Explain why solar energy is sustainable.

💡 Prompt Tips:

• Include only essential terms for better embedding proximity: Describe how solar panels generate electricity using photovoltaic cells.
• Avoid vague or verbose phrasing: Explain solar energy and its uses in a way that a normal person can understand and provide details.
• Use specific language to avoid diluting the context: Explain why solar energy is considered environmentally friendly and cost-effective.
• Avoid vague instructions that lack actionable context: Explain me solar energy.

💻 Code Tips: Write compact and clear PHPDocs to save tokens and improve context:

/**
 * Converts raw user input into a structured format.
 *
 * @param string $input Raw input data.
 * 
 * @return array{key: int, value: string} Structured output.
 */
function parseInput(string $input): array {
    $parts = explode(":", $input);

    return [
        'key' => (int)$parts[0],
        'value' => trim($parts[1])
    ];
}

• Use compact and descriptive documentation to maximize token efficiency: /** * Example: * Input: “42:Hello” * Output: [‘foo’ => 42, ‘bar’ => ‘Hello’] */

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🧭 3. Self-Attention and Structure: Prioritize Context

🧠 Theory: LLMs work with the principle of self-attention, where the input tokens are interrelated with each other to determine the relevance and context. This mechanism assigns importance scores to tokens, ensuring that the most relevant words and their relationships are prioritized.

⚙️ Technology:

• Self-attention layers: Compare each token with every other token in the input to generate an attention score.
• Multi-head attention: Allows the model to consider multiple perspectives simultaneously, balancing relevance and context.
• Pitfall: Too many irrelevant tokens dilute the attention scores, leading to distorted outputs.

⚙️ Optimizing Structure in Prompts:

• Structure Your Inputs: Use lists, steps, or sections to emphasize relationships: Compare the benefits of solar and wind energy: 1. Environmental impact 2. Cost-efficiency 3. Scalability
• Minimize Irrelevant Tokens: Keep prompts focused and free from extraneous details.

💡 Prompt Tips:

• Well-Structured: Organize tasks into sections: Explain the environmental and economic benefits of renewable energy in two sections: 1. Environmental 2. Economic
• Unstructured: Avoid asking everything at once: What are the environmental and economic benefits of renewable energy?

💻 Code Tips: In PHPDocs, organize information logically to enhance clarity and guide models effectively:

/**
 * Calculates the cost efficiency of renewable energy.
 *
 * Steps:
 * 1. Evaluate savings-to-investment ratio.
 * 2. Return a percentage efficiency score.
 *
 * @param float $investment Initial investment cost.
 * @param float $savings Annual savings.
 * 
 * @return float Efficiency percentage.
 */
function calculateEfficiency(float $investment, float $savings): float {
    return ($savings / $investment) * 100;
}

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🧹 4. Context Management and Token Limits

🧠 Theory: LLMs operate within a fixed token limit (e.g., ~8k tokens for GPT-4), encompassing both input and output. Efficiently managing context ensures relevant information is prioritized while avoiding irrelevant or redundant content.

⚙️ Technology:

• Chunking: Break long inputs into smaller, manageable parts: Step 1: Summarize the introduction of the report. Step 2: Extract key arguments from Section 1. Step 3: Combine summaries for a final overview.
• Iterative Summarization: Condense sections before integrating them: Summarize Section 1: Solar energy’s benefits. Summarize Section 2: Wind energy’s benefits. Combine both summaries.
• Pitfall: Excessive context can truncate critical data due to token limits.

💡 Prompt Tips:

• For large inputs, use step-by-step processing: Step 1: Summarize the introduction of the document. Step 2: Extract key arguments from Section 1. Step 3: Combine these points into a cohesive summary.
• Avoid presenting the full text in a single prompt: Summarize this 20-page document.
• Focus on specific sections or tasks: Summarize the introduction and key points from Section 1.

💻 Code Tips: Divide tasks into smaller functions to handle token limits better:

function summarizeSection(string $section): string {
    // Summarize section content.
}

function combineSummaries(array $summaries): string {
    // Merge individual summaries.
}

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🎨 5. Reasoning and Goals: Strengthen Prompt Direction

🧠 Theory: LLMs generate better results when the reasoning behind a task and its intended goal are explicitly stated. This guides the model’s probabilities toward meaningful and relevant outcomes.

⚙️ Technology:

• Explicit reasoning provides semantic depth, helping the model focus on the task’s purpose.
• Explaining the goal improves alignment with user expectations and narrows token probabilities.

💡 Prompt Tips:

• State the reason for the task and its goal: Explain renewable energy because I need to create an introductory guide for high school students.
• Avoid generic prompts without a clear goal: Describe renewable energy.

💻 Code Tips: Use PHPDocs to explain both the reasoning and expected outcomes of a function:

/**
 * Generates a detailed user profile report.
 *
 * This function is designed to create a comprehensive profile report based on user data inputs. 
 * It is useful for analytical dashboards requiring well-structured user insights.
 *
 * @param array{name: string, age: int, email: string} $userData The user data array.
 * 
 * @return string A formatted profile report.
 */
function generateProfileReport(array $userData): string {
    return sprintf(
        "User Profile:\nName: %s\nAge: %d\nEmail: %s\n",
        $userData['name'],
        $userData['age'],
        $userData['email']
    );
}

---

🛠️ 6. Iterative Refinement: Simplify Complex Tasks

🧠 Theory:
Breaking down complex tasks into smaller, manageable steps improves accuracy and ensures the model generates focused and coherent outputs. This method allows you to iteratively refine results, combining outputs from smaller subtasks into a complete solution.

⚙️ Technology:

• Chunking: Split large tasks into multiple smaller ones to avoid overwhelming the model.
• Validation: Intermediate outputs can be validated before moving to the next step, minimizing errors.
• Recombination: Smaller validated outputs are merged for the final result.

💡 Prompt Tips:

• For multi-step tasks, provide clear, incremental instructions: Step 1: Summarize the environmental benefits of solar energy. Step 2: Describe the cost savings associated with solar energy. Step 3: Combine these summaries into a single paragraph.
• Avoid handling complex tasks in a single step: Explain the environmental benefits and cost savings of solar energy in one response.

💻 Code Tips: Ask the LLM to create the code step by step and ask for confirmation after each step so that the LLM can focus on one aspect of the implementation at a time. Focus on one aspect of the implementation at a time.

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🔗 7. Cross-Contextual Coherence: Maintain Consistency

🧠 Theory:
LLMs lack persistent memory between interactions, making it essential to reintroduce necessary context for consistent responses across prompts. By maintaining cross-contextual coherence, outputs remain aligned and relevant, even in multi-step interactions.

⚙️ Technology:

• Use context bridging: Reference key elements from previous responses to maintain relevance.
• Store critical details in persistent structures, such as arrays or JSON, to reintroduce when needed.
• Avoid overloading with irrelevant details, which can dilute coherence.

💡 Prompt Tips:

• Reintroduce essential context from previous interactions: Based on our discussion about renewable energy, specifically solar power, explain the benefits of wind energy.
• Summarize intermediate outputs for clarity: Summarize the main benefits of renewable energy. Then expand on solar and wind energy.

💻 Code Tips: Use seperated files for Code-Examples that we can provide e.g. Custom GPTs so it can learn from learnings/findings this way.

---

🌍 8. Style and Tone: Adapt Outputs to the Audience

🧠 Theory: LLMs generate better responses when the desired style and tone are explicitly stated. By matching the tone to the audience, you can make content more engaging and effective.

⚙️ Technology:

• The model uses semantic cues in the prompt to adjust style and tone.
• Specific words and phrases like “formal,” “casual,” or “technical” help steer the model’s output.

💡 Prompt Tips:

• Specify the tone and audience: Write a technical explanation of solar panels for an engineering audience.
• Adjust the style for different contexts: Explain solar panels in a simple and friendly tone for kids.

💻 Code Tips: In PHPDocs, define the intended audience and tone to guide LLM-generated documentation:

/**
 * Calculates the total energy output of a solar panel system.
 *
 * Intended Audience: Engineers and technical experts.
 * Tone: Formal and technical.
 *
 * @param float $panelArea The total area of solar panels in square meters.
 * @param float $efficiency The efficiency rate of the solar panels (0-1).
 * @param float $sunlightHours Daily sunlight hours.
 * 
 * @return float Total energy output in kilowatt-hours.
 */
function calculateSolarOutput(float $panelArea, float $efficiency, float $sunlightHours): float {
    return $panelArea * $efficiency * $sunlightHours;
}

---

🔍 9. Fine-Tuning and Domain Expertise

🧠 Theory: Fine-tuning allows LLMs to specialize in specific domains by further training them on domain-specific datasets. This enhances their ability to generate accurate, relevant, and nuanced outputs tailored to specialized tasks or fields.

⚙️ Technology:

• Fine-tuning adjusts the weights of a pre-trained model by using a curated dataset that focuses on a specific domain.
• This process requires labeled data and computational resources but significantly improves task performance in niche areas.

💡 Prompt Tips:

• Use fine-tuning to simplify prompts for repeated tasks: Generate a legal brief summarizing the key points from this case.
• Without fine-tuning, include detailed instructions and examples in your prompt: Write a summary of this legal case focusing on liability and negligence, using a formal tone.

💻 Code Tips: When fine-tuning is not an option, structure your PHPDocs to include domain-specific context for LLMs:

/**
 * Generates a compliance report for renewable energy projects.
 *
 * This function creates a detailed compliance report tailored for regulatory agencies. It checks for adherence to
 * energy efficiency standards and sustainability guidelines.
 *
 * @param array<string, mixed> $projectData Details of the renewable energy project.
 * @param string $region The region for which the compliance report is generated.
 * 
 * @return string The compliance report in a formatted string.
 */
function generateComplianceReport(array $projectData, string $region): string {
    // Example report generation logic.
    return sprintf(
        "Compliance Report for %s:\nProject: %s\nStatus: %s\n",
        $region,
        $projectData['name'] ?? 'Unnamed Project',
        $projectData['status'] ?? 'Pending Review'
    );
}