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WeAreDevelopers LIVE • Aug 27, 2025

WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more

Chris Heilmann , Daniel Cranney , Raphael De Lio , Advocate At Redis

Stop flooding LLMs with redundant data. Leverage vector similarity search for semantic routing and caching to drastically reduce token costs while slashing processing times.

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#1 about 3 min

Welcome and updates from the WeAreDevelopers team

Recent platform updates include publishing conference talks and improving video topic searchability.

#2 about 7 min

Building visibility through open source and community engagement

Sharing technical knowledge publicly often leads to unexpected career opportunities and streamlined interviews.

#3 about 4 min

Challenging marketing claims with technical analysis and benchmarking

Proving accepted narratives wrong through independent testing demonstrates strong engineering capabilities.

#4 about 9 min

Mitigating language model costs with vector search patterns

Using vector databases to handle semantic embeddings reduces repetitive token costs and execution delays.

#5 about 9 min

Classifying social media text using semantic classification patterns

Leveraging vector similarity to categorize posts efficiently avoids the latency of external API calls.

#6 about 6 min

Optimizing tool calling in chatbots with semantic routing

Mapping user intents directly to execution functions by vectorizing predefined query triggers prevents unnecessary language model invocations.

#7 about 6 min

Implementing semantic caching for repetitive chatbot user queries

Storing query vectors and their corresponding responses prevents redundant processing for semantically identical questions.

#8 about 10 min

Tuning vector search accuracy and parameter extraction strategies

Applying retrieval optimizers and self-improvement feedback loops significantly improves the precision of semantic matches.

#9 about 7 min

Handling stale data and caching errors in chatbots

Strategies like time-to-live expirations and routing blocks prevent systems from serving outdated or hallucinated information.

#10 about 6 min

Managing vector database architecture and data lifecycle ownership

Enterprise vector workloads require proper distributed clustering and ongoing management by dedicated data engineers.

#11 about 5 min

Balancing convenience and costs in applied artificial intelligence

Developers should utilize robust optimization patterns instead of blindly relying on expensive automated subscription platforms.

Matching moments

3:37 min

Demonstrating semantic latency reductions using Spring AI configurations

1:48 min

Reusing language model responses through semantic database caching

3:13 min

Using semantic caching and pre-generated audio

Nathaniel Okenwa Nathaniel Okenwa · WWC 2024

1:39 min

Enabling semantic search with automated query vectorization

Iulia Feroli Iulia Feroli · LIVE

2:38 min

Enabling contextual responses with retrieval-augmented generation and vector databases

David Leconte David Leconte +1 · WWC 2024

3:08 min

Scaling semantic search with Astra DB and Apache Cassandra

David Leconte David Leconte +1 · WWC 2024

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Upcoming sessions on this topic

Open session

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KV Cache Is Not About Speed: It's About Surviving Inference Costs

David vonThenen

AI/ML Leader | Keynote Speaker | OSS Engineer & Developer Advocate | Agentic AI, Deep Learning, Production AI | Python, Go, C++

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Senior Software Engineer at Netflix

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Lead Software Engineer at Intuit

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Open session

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Enterprise AI Architect at American Chemical Society

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