Modular Prompt Template for Prompt-Based AI Systems (2025)
The Modular Prompt Template for Prompt-Based AI Systems (2025) is a robust framework that empowers designers, engineers, and PMs to create high-quality, adaptable prompts for large language models. By offering a structured approach that includes essential core components and optional advanced modules, this template addresses common challenges like prompt drift, versioning, and the need for personalization in AI applications. With its clear categorization and explanatory comments, it simplifies the prompt engineering process, making it easier to tailor interactions across different use cases, such as developing conversational agents or automating complex workflows. Utilizing this template not only enhances the effectiveness of AI systems but also promotes best practices in prompt design, ultimately optimizing user satisfaction and system performance.
Spec
A ready-to-use, modular prompt template for designing robust, flexible prompts for any prompt-based AI system.
Modular Prompt Template for Prompt-Based AI Systems (2025)
A flexible, extensible, and structured prompt template designed for prompt-based AI systems using large language models (LLMs). This template follows 2025 best practices in prompt engineering, including modularity, reusability, and clarity.
📁 Metadata
- Title: Modular Prompt Template
- Version: 1.0
- Last Updated: 2025-07-15
- Author: Luis Alberto Martinez Riancho (@arenagroove)
- Affiliation: Less Rain GmbH
- Tags: prompt-engineering, LLM, modular, AI, template
- License: MIT
- Platform Compatibility:
- Not all modules are supported by every LLM provider—check your platform’s documentation.
[ADVANCED]tags indicate modules requiring advanced LLM capabilities.
🧠 Purpose
This template provides a modular scaffold for building high-quality prompts that can be adapted across domains, tasks, and user contexts. It supports both core components (essential for most tasks) and optional modules (for advanced control, personalization, and robustness).
✅ Use Cases
- Prompt engineering for LLM-based assistants, agents, or chatbots
- Workflow automation and task orchestration
- Prompt versioning and drift management
- Teaching or documenting best practices in prompt design
🧩 Structure
Each section is clearly marked as:
- [CORE] – Essential for most prompt-based tasks
- [OPTIONAL] – Add for flexibility, specificity, or robustness; include as needed and in any order
- [CONTEXT ENGINEERING] – Indicates modules reflecting advanced context-centric practices
- [ADVANCED] – Requires advanced LLM features; check platform support
Explanatory comments are included under each heading for guidance
Instruction [CORE]
Main task for the model; clear, actionable, and specific.
Instruction:
Summarize the following article in three bullet points, focusing on key facts only.
Context [CORE] [CONTEXT ENGINEERING]
All relevant background, data, or input needed for the task. Use clear delimiters for large blocks of text.
Context:
Article: """
[Paste article text here]
"""
Role/Persona [CORE]
Assigns expertise, tone, or perspective to the model.
Role/Persona:
You are an experienced business analyst writing for a corporate audience.
Output Constraints [CORE]
Specifies output format, length, style, and restrictions.
Output Constraints:
- Format: Bullet points
- Max 50 words
- No subjective language
Examples (Few-Shot) [CORE for all but simplest tasks]
Anchors expected output; always include for ambiguous or complex tasks.
Examples:
Input: "The company reported record profits..."
Output:
- Record profits reported for Q2.
- Revenue grew by 20% year-over-year.
- New products were key growth drivers.
Lens [OPTIONAL]
Applies a specific analytical or stylistic filter (e.g., "risk management lens").
Lens:
Analyze the article through a risk management lens.
Audience [OPTIONAL]
Specifies the intended reader/user (e.g., "for non-technical executives").
Audience:
Intended for senior executives with limited technical background.
Style Guide [OPTIONAL]
Explicitly sets the writing style or tone (e.g., "formal academic style").
Style Guide:
Use concise, persuasive business language.
Drift Awareness [OPTIONAL] [CONTEXT ENGINEERING] [ADVANCED]
Detects and reports changes in meaning or output over time (prompt, concept, or output drift).
Drift Awareness:
- Prompt Drift: Changes due to prompt/model updates.
- Concept Drift: Shifts in meaning or context.
- Output Drift: Divergence from baseline summaries.
Compare your output to the baseline and flag any drift.
Assumptions & Limitations [OPTIONAL] [CONTEXT ENGINEERING]
Lists assumptions or known gaps; promotes transparency.
Assumptions & Limitations:
Assume all data is current as of 2025. If any data is missing, state “Data not available.”
Step-by-Step Reasoning [OPTIONAL]
Guides the model through multi-step logic or chain-of-thought.
Step-by-Step Reasoning:
1. Identify the three most important facts.
2. Exclude opinions or minor details.
3. Phrase each point concisely.
Tool/Function Invocation [OPTIONAL] [CONTEXT ENGINEERING] [ADVANCED]
Specifies tool/API call syntax per platform; clarify fallback behavior if invocation fails.
Tool/Function Invocation:
- OpenAI: call function_name(args)
- Anthropic: [TOOL: tool_name] input
If calculations are required, use the calculator API and cite results.
