Put what you have learned in this course into practice with the structured exercise below. Working through it end-to-end is the single best way to consolidate the concepts.

Exercise: Semantic Search Implementation in the Real World

Pick one realistic scenario from your own work, study, or curiosity and apply the techniques covered in earlier lessons. Aim for a small, completable artifact — a notebook, a working script, a written design, or a side-by-side comparison.

Suggested Workflow

1. Frame the goal: write a one-sentence problem statement that the techniques in this course can address.
2. Pick the right tool: revisit the lessons above and choose the specific method, model, or pattern that best fits.
3. Build an MVP: produce the simplest version that proves the approach works. Keep it small.
4. Evaluate: how did it perform? What surprised you? What broke?
5. Iterate: refine one decision (prompt, parameter, tool) and re-evaluate.

Self-Check Questions

• Could you explain the chosen approach to a teammate in under two minutes?
• Where might this break in production? What guardrails would you add?
• What is the next thing you would try if you had another day?

Stretch Goal

Document your run as a short write-up. Compare against alternative approaches and note the trade-offs.

This practice loop converts passive reading into durable skill — the exact transfer-of-learning step most learners skip.

Concrete Starter Project: Build a Documentation Search System

Goal: Build a semantic search engine for a technical documentation set (e.g., the Next.js or FastAPI docs) with hybrid search and reranking.

Steps:

1. Download the documentation as markdown files from the project's GitHub repository.
2. Chunk each file into 256-token segments with 32-token overlap.
3. Prepend the document title and section heading to each chunk before embedding.
4. Index into a pgvector-enabled PostgreSQL database using text-embedding-3-small.
5. Build a search endpoint that combines BM25 full-text search (via PostgreSQL tsvector) with vector similarity, merged using RRF.
6. Add a Cohere reranker as the final stage before returning results.

Extend it:

• Build a RAGAS evaluation dataset by generating synthetic questions from each documentation chunk.
• Compare search quality across three chunk sizes (128, 256, 512 tokens).
• Add HyDE: generate a hypothetical answer to the query and embed that instead of the raw query.

What you will learn: The complete semantic search stack — chunking strategy decisions, hybrid retrieval, reranking, and systematic quality evaluation.