Module 9 · Real-World GenAI Projects
Project Guide: Semantic Search
Semantic search projects use embeddings and similarity retrieval to find relevant information despite vocabulary differences.
This reading lesson is complete and can be studied independently. The video will be added here when it is published.
Core idea
Project Guide: Semantic Search
Semantic search projects use embeddings and similarity retrieval to find relevant information despite vocabulary differences.
Visual workflow
How the parts connect
Every boundary is an opportunity to validate inputs, permissions, quality, and failure handling.
Worked example
Apply the concept
Production and responsibility checklist
Quality
Correctness, relevance, coverage, and consistency.
Security
Identity, data, tools, networks, and logs.
Fairness
Impacted groups and meaningful failure differences.
Operations
Latency, errors, drift, quotas, and availability.
Human control
Review, escalation, override, and accountability.
Economics
Usage drivers and measurable business value.
Knowledge check
What to remember
- Prepare searchable content
- Create embeddings
- Retrieve similar items
- Measure relevance
- Identify the requirement, constraint, risk, and verification method before choosing a technology.
Key takeaways
- Prepare searchable content
- Create embeddings
- Retrieve similar items
- Measure relevance
Check your understanding
- Can you explain this concept in two sentences without using jargon?
- Can you identify the input, process, output, and validation step in the example?
- Can you name one suitable use case and one case where another approach is better?
- Which risk or limitation should a responsible implementation address?
Research references
Public sources and further reading
This lesson is original educational writing informed by the public references below. Use the sources to explore definitions, technical details, and current AWS exam objectives.