Portfolio Projects That Prove AI Product Engineering Skills

TL;DR

• One strong project beats five half-done ones. Pick one. Ship it well.
• Focus on: clear problem, your role, technical choices, outcomes, and what you'd do differently.
• Hiring managers want to see: you can scope, build, and iterate on AI features.

Five project ideas. Pick one (or two). Make it excellent.

1. RAG-Powered Doc Q&A

What: Q&A over a document set (API docs, internal wiki, or public corpus like gov data).

Shows: Chunking, embeddings, retrieval, prompt design, basic eval.

Stack: LangChain, LlamaIndex, or raw API + vector DB. Deploy: Streamlit, Gradio, or simple web app.

Outcome to highlight: "Answered X% of test questions correctly. Retrieval latency under Y ms."

2. AI-Augmented Search

What: Search that combines keyword + semantic. User types query; gets ranked results from both.

Shows: Hybrid search, embedding integration, UX for "AI-enhanced" (not "AI replaced").

Stack: Elasticsearch/Meilisearch + vector extension, or Pinecone + keyword filter.

Outcome: "Improved relevance for long-tail queries by X%. Fallback when semantic fails."

3. Support Ticket Triage Bot

What: Ingest ticket text → suggest category, priority, or routing.

Shows: Classification, prompt design, handling low-confidence, human-in-the-loop.

Stack: LLM API (classification or few-shot). Optional: fine-tune small model for speed/cost.

Outcome: "Reduced manual triage time by X%. Accuracy Y%."

4. Meeting Notes Summarizer

What: Upload transcript or recording → get summary, action items, key decisions.

Shows: Summarization, structured output (JSON), streaming (if you do it token-by-token).

Stack: Whisper for transcription (or use pre-transcribed), LLM for summary.

Outcome: "Generated summaries in under X sec. Users rated helpfulness Y/5."

5. Semantic Code Search

What: Search a codebase by meaning. "Find where we handle auth token refresh."

Shows: Code embeddings, chunking strategy, retrieval over non-docs.

Stack: CodeBERT, StarCoder embeddings, or generic embeddings on code. Vector DB.

Outcome: "Found relevant snippets in X% of test queries vs Y% for grep."

What Makes a Portfolio Project Stand Out

• Problem is clear. "Users can't find answers in our docs" — not "I wanted to try RAG."
• You made trade-offs. "Chose smaller model for latency; accepted slight quality drop."
• You measured. Accuracy, latency, cost. Numbers.
• You documented. README, architecture, what you'd improve.

# RAG Doc Q&A

**Problem:** Users can't find answers in our 500-page docs.

**My role:** End-to-end — chunking, embeddings, retrieval, prompt design.

**Tech:** Python, OpenAI embeddings, pgvector, GPT-4o-mini.

**Outcomes:** 85% of test questions answered correctly. P95 latency 800ms.

**What I'd do differently:** Hybrid search for ambiguous queries.

Do This Next

1. Pick one project from the list (or a close variant). Scope it to 1–2 weeks.
2. Define success — What's the minimal outcome? "Answer 5 test questions correctly."
3. Ship and document — Deploy somewhere. Write it up. Add to portfolio and LinkedIn.