WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more

What if you could answer questions and route requests without ever calling an LLM? Explore three vector search patterns for building faster, more cost-effective AI applications.

#1about 8 minutes

Getting hired through open source and passion projects

Hear how contributing to open source and sharing your work publicly can lead directly to job opportunities in developer advocacy.

#2about 5 minutes

How critical analysis can accelerate your career

Discover how publicly analyzing and improving upon existing technologies can make you a highly visible and attractive candidate for top companies.

#3about 3 minutes

The hidden costs of large LLM context windows

Understand why simply using larger context windows in models like GPT-5 is not a scalable or cost-effective solution for production applications.

#4about 3 minutes

A quick primer on vectors and vector search

A brief explanation of how text is converted into numerical vectors to represent its semantic meaning, enabling similarity searches.

#5about 9 minutes

Using semantic classification to categorize text

Learn how to use a vector database with reference examples to classify text, avoiding costly LLM calls for simple categorization tasks.

#6about 5 minutes

Implementing semantic routing for tool calling and guardrails

Discover how to use semantic routing to direct user prompts to the correct function or to block inappropriate topics without involving an LLM.

#7about 6 minutes

Reducing latency and cost with semantic caching

Implement semantic caching to store and retrieve answers for semantically similar user questions, drastically reducing redundant LLM calls and improving response time.

#8about 6 minutes