As prompt engineering shifts from niche curiosity to core skill in AI-driven development, professionals are now expected to demonstrate real project work—not just theoretical knowledge. Whether you're applying for roles in LLM fine-tuning, AI tooling, or conversational UX design, having a well-structured prompt engineering portfolio is one of the most effective ways to stand out.
In this article, you'll learn how to organize, format, and present your prompt projects on GitHub or Notion to reflect not only creativity, but also technical rigor, reproducibility, and job-readiness.
Why You Need a Portfolio for Prompt Engineering
Prompt engineering isn’t just about writing clever instructions—it’s about:
Understanding model behavior and constraints
Designing structured, reusable prompt patterns
Testing outputs for consistency and performance
Integrating prompts into tools, pipelines, and apps
Employers want to see evidence of your reasoning, iteration process, and results, especially in applied settings like chatbots, data extraction, or content generation.
A portfolio gives you a place to document your projects, compare outputs, and show your workflow—while also making your skill development public and searchable.
What a Strong Prompt Engineering Portfolio Should Include
Before choosing between GitHub or Notion (or both), clarify the essential components every prompt portfolio should feature:
1. Project Overview
Each project should begin with a short summary:
What problem were you solving?
What was the goal of the prompt(s)?
What model or tool was used (GPT-4, Claude, Gemini, etc.)?
2. Prompt Iteration Logs
Show multiple versions of the prompt as you refined them
Annotate what changed and why
Include examples of failed attempts (this reveals critical thinking)
3. Input-Output Pairs
Include clear examples of inputs and corresponding outputs
For classification or generation tasks, show consistency across edge cases
4. Prompt Templates
If you designed reusable templates (e.g., using curly braces for variable injection), include those. Great for showing prompt design patterns and abstraction.
5. Tooling and Framework Integration
Describe any prompt chaining tools used (LangChain, PromptLayer, LlamaIndex)
Show how the prompt fits into a larger pipeline (API call, UI interface, agent flow)
6. Evaluation Method
Did you use human feedback, scoring, or eval libraries (TruLens, RAGAS)?
Include screenshots or metrics when possible
Structuring a GitHub Prompt Portfolio
GitHub is the right choice if you want your portfolio to be technical, version-controlled, and open-source contributor–friendly.
Folder Structure:
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/prompt-portfolio │ ├── README.md ├── project-01-content-summarization/ │ ├── prompt_versions.md │ ├── input_output_samples.md │ ├── model_config.json │ ├── notes.md │ ├── project-02-code-refactoring/ │ ├── original_code.py │ ├── refactored_output.md │ ├── prompt_iterations.md │ ├── assets/ │ ├── screenshots/ │ ├── diagrams/
Best Practices:
Use Markdown to explain each step
Commit changes with messages like
refactored prompt for tone balanceLink to Hugging Face Spaces, ChatGPT custom GPTs, or Replit if applicable
Include a main README.md that outlines your learning goals, models used, and top projects
Ideal For:
Technical recruiters and hiring managers
Showcasing your process in versioned stages
Integration with CI tools or lightweight frontends
Structuring a Notion Prompt Portfolio
Notion is ideal for nonlinear storytelling, visual context, and UX-focused presentation. It’s especially effective for freelance, product design, or marketing-aligned prompt engineers.
Suggested Layout:
Homepage:
Portfolio summary
Links to top projects
Resume + contact info
Each Project Page Includes:
Project title and icon
Objective and scope
Prompt evolution table (with columns for version, change reason, outcome)
Side-by-side screenshots of outputs
Embedded Loom videos or demos
Evaluation commentary
Tags (e.g.,
GPT-4,classification,chatbot,LangChain,semantic search)
Bonus Feature:
Add a Prompt Pattern Gallery page where you showcase reusable strategies like:
Chain of Thought (CoT)
Few-shot classification
JSON parsing prompts
Instruction-following vs. open-ended generation
Ideal For:
Human-centric or creative projects
Visual storytelling
Quick navigation between use cases and outcomes
Portfolio Ideas for Beginners and Intermediates
Not sure what to include? Start with projects that solve common problems but allow room for creative iteration:
Summarize Legal or Policy Texts with fidelity and length constraints
Refactor Legacy Code using prompt-guided transformation
Build a Structured Q&A Bot using retrieval-augmented generation
Extract Structured Data from Unstructured Inputs (e.g., receipts, resumes)
Create Style-Conscious Content Generators (e.g., tone-specific emails)
Show your reasoning, prompt design, and evaluation—not just the end result.
Tips to Make Your Portfolio Stand Out
1. Focus on Real-World Use Cases
Solve a real problem: marketing copy, resume review, FAQ generation, etc. Bonus points if you integrate the prompt into a tool or app.
2. Show Prompt Evolution
Demonstrate how your prompts improve across versions. This tells hiring managers that you understand iteration and optimization.
3. Visualize Differences
Use tables, screenshots, or side-by-side comparisons to highlight how output changed with different prompt designs.
4. Include Commentary
Write short explanations about what worked and why. This reveals your analytical thinking and practical judgment.
5. Use Both GitHub and Notion If You Can
Link between them. Use GitHub for versioned, technical code. Use Notion to tell the story behind your prompt work.
Final Thoughts: Your Portfolio Is a Living Showcase of Your Prompt Thinking
Prompt engineering is a rapidly evolving skill, and your portfolio should evolve with it. By documenting your projects on GitHub or Notion in a clear, structured, and reproducible way, you show more than creativity—you prove that you can think like a system designer, debug like a developer, and evaluate like a product strategist.
Whether you're applying for a job, launching a freelance service, or just building in public, a strong portfolio is your ticket to visibility and credibility in the AI ecosystem.
FAQs
Do I need to include code in my prompt engineering portfolio?
Only if it adds value. If you're integrating prompts into APIs or apps, code helps. For standalone prompt design, Markdown or Notion-based logs are fine.
Should I include failed prompt attempts?
Yes—explaining why something didn’t work is a powerful way to show your reasoning process and learning agility.
How many projects should I include?
Aim for 3 to 5 well-documented projects that show range (classification, summarization, generation, evaluation, etc.).
What tools help manage prompt workflows?
Try LangChain, PromptLayer, or TruLens for evaluation and logging. Use Replit or Colab notebooks for interactive prototypes.
Can this portfolio replace a resume?
Not entirely, but it complements it. Add your portfolio to your resume, LinkedIn, and job applications to give reviewers immediate proof of your skill set.