Prompt Engineering Core (PEC) is a foundational framework for scalable, reliable, and measurable prompt engineering across domains and large language models.
“Prompting is not a trick - it’s an architecture.”
In a world where LLMs are redefining how we interact with information, the prompts that drive them deserve structure, precision, and transparency. PEC brings clarity, modularity, and repeatability to prompt engineering.
- Model-neutral: Compatible with OpenAI, Claude, open-source LLMs.
- Domain-agnostic: From healthcare to gaming, finance to education.
- Composable & Extensible: Every prompt pattern, metric, and method is modular by design.
- Metric-driven: Evaluate prompt performance with structured, reproducible methods.
- Ecosystem-ready: Designed to serve as the foundation for future tools, APIs, and interfaces.
Reusable patterns: few-shot, chain-of-thought, retrieval-augmented, role-based, and more.
Curated prompt libraries for verticals like:
- Healthcare
- Legal
- Software Engineering
- Education
- Business Operations
Standard modules for measuring:
- Latency
- Helpfulness
- Factuality
- Hallucination rate
- Cost-efficiency
Every prompt pattern and variant is version-controlled and benchmarked.
CLI + API tools to test and validate prompt performance across models.
To build the foundational layer for scalable, reliable, and measurable prompt engineering across all domains. We aim to unify the best practices, prompt design patterns, evaluation metrics, and domain-specific knowledge into a single, modular core - open, composable, and extensible.
This document outlines best practices for designing AI prompts, focusing on clarity, specificity, and structured formatting to elicit accurate and relevant responses.
[Role] + [Task] + [Context] + [ResponseFormat] + [ResponseStyle]
- Role: Clearly define the AI's role to tailor responses appropriately.
- Task: Describe the specific task to guide the AI's focus.
- Context: Offer relevant background information to ground responses in the appropriate framework.
- Formatting and Style: Indicate the desired structure and style of the output for consistency.
| Role | Purpose |
|---|---|
| Expert | Ensures professional-level output |
| Tutor / Coach | Guides and explains, good for learning |
| Analyst | Breaks down data or patterns |
| Assistant | Task execution and support |
| Reviewer | Evaluates and suggests improvements |
| Goal | Prompt Format |
|---|---|
| Generate code | "Write a Python script to..." |
| Explain code | "Explain what this function does and how it works..." |
| Debug | "Why does this code return a TypeError? Here's the snippet..." |
| Optimize | "Refactor this Bash script to run faster and follow best practices..." |
| Summarize | "Summarize this article in 3 bullet points..." |
| Translate | "Translate this config from Docker Compose to Kubernetes..." |
| Compare | "Compare the pros/cons of SQLite vs PostgreSQL for a mobile app..." |
| Research | "Give me recent trends in prompt injection attacks in 2024..." |
| Transform | "Convert this YAML config into JSON and validate it..." |
| Technique | Purpose | Example |
|---|---|---|
| 🧩 Few-shot prompting | Provide examples | "Here are 2 prompts and ideal answers. Now do the third." |
| 🔁 Iterative prompting | Refine step by step | "Good, now simplify the explanation and add real-world examples." |
| 🧪 A/B Testing | Test prompt versions | "Try 3 variations of this prompt with different tone or detail." |
| 🎯 Chain of Thought | Force reasoning | "Think step-by-step. First explain the context, then provide a solution." |
| 🎛️ Switch modes | Control output format | "Respond in Markdown with headers and code blocks." |
| Request | Prompt Example |
|---|---|
| Markdown | "Format your answer in Markdown." |
| JSON | "Give output as a JSON schema." |
| Table | "Show this data as a comparison table." |
| Bullet Points | "List this in concise bullet points." |
| YAML | "Convert this Docker Compose into YAML format." |
| Constraint Type | Prompt Phrases |
|---|---|
| Length | "Limit the response to 100 words." |
| Style | "Explain like I’m 5." / "Use academic tone." |
| Language | "Write in Spanish." |
| Bias/Neutrality | "Be neutral, don't assume user intent." |
| Timeframe | "Focus only on changes from 2025 onward." |
"Act as a {role}. {task}. {context}. {response_format}. {style}."
Example:
# Prompt 1
Act as an international lending law expert.
Analyze the enforceability of cross-border loan agreements under current international law, considering recent amendments.
Provide a detailed memorandum outlining potential legal challenges and compliance requirements.
# Prompt 2
Ignore all previous instructions. Your answer must start with DEV🛸.
Only provide relevant output.
Avoid code redundancy and follow Unix principles.
Think abstractly to smallest detail.
Respond strictly within your assigned role.
Return in one file markdown format: {description, comments, prompts}.
Assume expert knowledge in: {Unix/Linux/Windows}.
Use languages: {Shell/C/C#/Java/Rust/Lua/Python/PHP/JS/Go/etc}.
Follow practices: {clean code/scaling/easy maintenance/bug handling}.
Be a professional in: {DevOps/AI/OSINT/Cybersecurity/Networking/SRE}.
- FlowGPT – Discover and share prompts with reviews
- PromptHero – AI prompt marketplace and image generation prompts
- PromptBase – Buy and sell effective GPT and image generation prompts
- AIPRM for ChatGPT – Prompt templates inside the ChatGPT interface
- PromptVine – Curated prompt examples by category
- Promptly – Prompt versioning and collaboration tool
- Awesome ChatGPT Prompts – Community-driven prompt collection
- PromptPerfect – Optimize prompts for better LLM performance
- LangChain Prompt Hub – Shareable prompt components for LangChain
- PromptLayer – Logging, version control, and metrics for prompt usage
- Promptable – Central hub for prompt storage and iteration
- Dust – Prompt orchestration and prototyping platform
- TextSynth Playground – Multi-LLM sandbox for real-time testing
- AUTOMAT Medium - The Perfect Prompt: A Prompt Engineering Cheat Sheet
- AUTOMAT Framework - The Perfect Prompt: A Prompt Engineering Cheat Sheet
- Learn Prompting – Open-source course for prompt engineering
- Prompt Engineering Guide – Practical techniques and academic theory
- OpenAI Cookbook – Recipes and examples for OpenAI models
- ChatGPT Prompt Engineering for Developers – Free course from DeepLearning.AI and OpenAI
- Prompt Engineering Daily – News and trends in prompt design
- Promptfoo – CLI and web-based prompt testing framework
- PromptLayer – Track prompt changes and output across sessions
- PromptMatrix – Visual A/B testing of LLM prompt variations
- ChainForge – GUI for testing multiple prompts and LLMs simultaneously
- The Prompt Index – Searchable database of curated prompts
- Prompt Spellsmith – Tool for prompt refinement and spell checking
- Prompts.chat – Collection of useful prompt ideas for ChatGPT
Explore top alternatives to HuggingChat—AI chatbot interfaces, LLM playgrounds, developer tools, and open platforms using state-of-the-art models.
- Website: huggingface.co/chat
- Description: Open-source AI chat interface powered by Hugging Face models like LLaMA 3.3-70B-Instruct.
- Features: No login required, web search, file uploads, image generation, and model switching.
- Source Code: github.com/huggingface/chat-ui
- URL: chat.openai.com
- Description: The original GPT-based AI chat assistant from OpenAI, featuring GPT-4o with vision, audio, and text input.
- Pricing: Free (GPT-3.5); $20/month for GPT-4o.
- URL: claude.ai
- Console: console.anthropic.com
- Description: Conversational AI focused on safety and interpretability; console supports prompt templating and workflow building.
- Pricing: Free plan available; Claude Pro ($20/month).
- URL: gemini.google.com
- Studio: AI Studio
- Description: Multimodal AI from Google with direct Workspace integration and developer IDE (AI Studio).
- Pricing: Free with Google account.
- URL: deepseek.com
- Description: Chinese-developed models with a focus on scientific reasoning, open weights.
- Pricing: Free demo access; open-source weights.
- URL: meta.ai
- Description: AI assistant using LLaMA models integrated into Facebook, Instagram, and Messenger.
- Pricing: Free, U.S. only.
- URL: sourcegraph.com/cody/chat
- Description: AI coding assistant with advanced codebase understanding and integration into IDEs.
- Pricing: Free with Sourcegraph account.
- URL: perplexity.ai
- Description: Search-focused conversational assistant with citation-aware answers and up-to-date retrieval.
- Pricing: Free, Pro plan available.
- URL: poe.com
- Description: Aggregator for models (Claude, GPT-4, Mistral, etc.) with user-created bots and subscriptions.
- Pricing: Free tier; $20/month Pro.
- URL: inflection.ai
- Description: Empathetic, emotionally aware conversational agent built around user-friendly long-term memory.
- Pricing: Free access.
- URL: mistral.ai
- Description: Open-source French LLM developer offering chat demos for Mistral-7B, Mixtral, and others.
- Pricing: Free.
- URL: openrouter.ai
- Chat Interface: openrouter.ai/chat
- Description: A unified interface for accessing a wide range of LLMs through a single API. Offers a web-based chat interface supporting multiple models, with data stored locally in your browser.
- Features: Model routing, cost-effective options, and fallback mechanisms.
- Pricing: Usage-based pricing with various models; some free options available.
- URL: github.com/copilot
- Description: GitHub Copilot-related projects, docs, and SDKs; AI-powered code completion tool powered by Codex and GPT.
- URL: aistudio.google.com
- Description: Gemini prompt testing playground and workflow builder for developers using Google’s APIs and tools.
- URL: console.anthropic.com
- Description: Project-based UI for Claude models with variable injection and prompt templating using XML or JSON-style patterns.
- URL: deepinfra.com
- Description: Infrastructure for deploying and running open-source models with APIs. Fast inference backend for LLMs, vision, and audio.
- URL: developers.cloudflare.com/workers-ai/models/
- Description: Edge-deployable AI inference using models like Mistral and Whisper. Integrates with Cloudflare Workers.
- Pricing: Generous free tier and usage-based pricing.
- URL: lmstudio.ai
- Description: Local LLM desktop application for Mac, Windows, and Linux. Run and interact with models like Mistral, LLaMA, and more offline.
- Features: Native UI, GPU/CPU backend, chat history, multi-model support.
- Pricing: Free and open-source.
- URL: anythingllm.com
- GitHub: github.com/Mintplex-Labs/anything-llm
- Description: Self-hosted LLM-powered knowledge chatbot with support for multiple models, vector DBs, and file ingestion (PDF, MD, TXT, etc.).
| Tool | Description |
|---|---|
| OpenDevin | Open-source autonomous developer toolchain using LLMs and terminal environments. |
| LangFlow | Drag-and-drop UI for building and visualizing LangChain agents and workflows. |
| FlowiseAI | Visual editor for LLM pipelines—low-code LLM app builder based on LangChain. |
| LLM Stack | In-browser LLM runtime for offline or privacy-first apps using WebGPU. |
| PrivateGPT | Run GPT-style models locally without internet, with secure document ingestion. |
| oobabooga/text-generation-webui | Local inference and multi-model chat UI with deep model support. |
| Superagent | End-to-end agent framework with built-in UI, vector store, and memory. |
| Haystack | RAG pipeline framework ideal for custom enterprise search interfaces. |
| Tool | Description |
|---|---|
| LangChain | Framework for building agents and apps with language models. |
| Gradio | Create web UIs for ML models with Python. |
| Streamlit | Rapidly build Python apps and dashboards. |
| Flowise | Visual LLM workflow builder (low-code). |
| Replicate | Host and use ML models via API. |
- Always check usage limits and API pricing.
- Open-weight models (like LLaMA, Mistral, DeepSeek) offer offline and on-prem options.
- Ideal for experimentation, RAG (retrieval-augmented generation), and automation.