Semantic search enables GenAI systems to retrieve information based on intent and context, but many teams struggle to design, validate, and tune these systems beyond basic embedding demos.
To win, your semantic search must be grounded in clear principles, validated relevance, and context-aware retrieval pipelines.
Teams adopting semantic search frequently encounter:
- Unclear foundations: Use embeddings without a shared understanding of what semantic search is—and is not—designed to solve.
- Fragile pipelines: Build similarity workflows that work in isolation but fail under real query and document diversity.
- Unvalidated relevance: Rely on intuition or offline metrics instead of structured human feedback to assess quality.
Poorly designed semantic search will return plausible but irrelevant results, weakening trust in GenAI systems.
In this hands-on workshop, your team designs, implements, and evaluates semantic search pipelines with a focus on relevance, validation, and context awareness.
- Define the core principles that distinguish semantic search from lexical approaches.
- Select embedding models suited to enterprise search requirements and data types.
- Design query-document similarity pipelines that support consistent retrieval.
- Validate relevance using structured human feedback and evaluation techniques.
- Tune retrieval behavior to improve context awareness across varied queries.
- Defining Semantic Search Principles
- Selecting Embedding Models for Search
- Designing Query-Document Similarity Pipelines
- Validating Relevance Using Human Feedback
- Tuning Retrieval for Context Awareness
- Establish a clear mental model for when and how to use semantic search.
- Select embedding models aligned to search goals and constraints.
- Design robust similarity pipelines for real-world enterprise data.
- Apply human feedback to validate and improve retrieval relevance.
- Tune semantic search systems for stronger contextual understanding.
Who Should Attend:
Solution Essentials
Virtual or in-person
8 hours
Intermediate; familiarity with search or embeddings recommended
Embedding models, similarity pipelines, evaluation workflows in a guided environment