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Practitioner Portfolio Template

Use this template to document your production AI feature for Practitioner portfolio.

Feature: [Name]​

Date Deployed: [When it went to production]

Environment: [Where was this work done]

Status: [In production / Deprecated / etc.]

1. Overview​

Problem Statement​

What problem does this feature solve?

[Describe the business problem or user need]

Solution Summary​

Brief description of what you built.

[2-3 sentences describing the solution]

Periodic Table Elements Used​

ElementRole in System
[e.g., Rg - RAG][How it's used]
[e.g., Vx - Vector DB][How it's used]
[e.g., Fc - Function Calling][How it's used]

2. Architecture​

System Diagram​

Draw or describe the architecture.

[ASCII diagram or description of components and data flow]

Example:
User Query → API Gateway → RAG Service → Vector DB
                              ↓
                           LLM API
                              ↓
                         Response + Sources

Key Components​

ComponentTechnologyPurpose
[e.g., Vector Store][e.g., Pinecone][e.g., Document storage and retrieval]
[e.g., Embedding Model][e.g., text-embedding-3-small][e.g., Generate query embeddings]
[e.g., LLM][e.g., GPT-4 Turbo][e.g., Generate responses]

Design Decisions​

Decision 1: [e.g., Why RAG over Fine-tuning]

  • Options considered: [List alternatives]
  • Choice made: [What you chose]
  • Reasoning: [Why]

Decision 2: [e.g., Chunking Strategy]

  • Options considered: [List alternatives]
  • Choice made: [What you chose]
  • Reasoning: [Why]

Add more decisions as relevant

3. Implementation​

Key Code Patterns​

Show important code patterns (sanitize any sensitive info).

# Example: RAG retrieval logic
def retrieve_context(query: str, k: int = 4) -> list[Document]:
    """Retrieve relevant documents for a query."""
    # Your implementation pattern here
    pass

Frameworks/Libraries Used​

LibraryVersionPurpose
[e.g., LangChain][e.g., 0.1.x][e.g., RAG pipeline orchestration]
[e.g., Chroma][e.g., 0.4.x][e.g., Local vector storage]

Configuration​

Key configuration decisions.

ParameterValueWhy
[e.g., Chunk size][e.g., 512 tokens][e.g., Balanced context vs. precision]
[e.g., Top-k retrieval][e.g., 4][e.g., Enough context without noise]
[e.g., Temperature][e.g., 0.3][e.g., Consistent, factual responses]

4. Challenges and Solutions​

Challenge 1: [Title]​

Problem:

[Describe what went wrong]

Investigation:

[How you diagnosed the issue]

Solution:

[How you fixed it]

Lesson:

[What you learned]

Challenge 2: [Title]​

Repeat the pattern above

5. Results​

Quantitative Metrics​

MetricTargetActualNotes
[e.g., Retrieval precision][e.g., above 80%][e.g., 85%][e.g., Measured on test set]
[e.g., Response latency (p50)][e.g., under 2s][e.g., 1.4s][e.g., Production average]
[e.g., Monthly cost][e.g., under $500][e.g., $320][e.g., At current volume]

Qualitative Feedback​

What did users say?

[Quote or summarize user feedback]

Before/After Comparison​

If applicable, show improvement over previous state.

AspectBeforeAfter
[e.g., Time to find answer][e.g., 10 min manual search][e.g., 30 sec with AI]

6. Reflection​

What Worked Well​

  • [Success 1]
  • [Success 2]
  • [Success 3]

What I'd Do Differently​

  • [Improvement 1]
  • [Improvement 2]
  • [Improvement 3]

Skills Developed​

Which Practitioner skills did this project develop?

  • Function calling implementation
  • Vector database setup and optimization
  • RAG pipeline construction
  • Context management
  • Framework proficiency
  • Production deployment
  • Cost optimization
  • Other: [specify]

7. Supporting Materials​

Link to additional documentation (if available and shareable):

  • Code repository: [link or "internal only"]
  • Architecture document: [link or "available on request"]
  • Demo video: [link or "can demonstrate live"]
  • Monitoring dashboard: [link or "internal only"]

Submission Checklist​

Before submitting, confirm:

  • Feature is deployed to production (internal counts)
  • Architecture decisions are documented with reasoning
  • Challenges and solutions are described honestly
  • Metrics are included (even if limited)
  • Code patterns shown (sanitized if needed)
  • Reflection includes both successes and improvements

Remember: We learn as much from challenges as from successes. Be honest about what didn't work.