Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)

Large context windows aren't the answer. Learn three vector search patterns to slash your LLM costs and latency.

Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)
#1about 3 minutes

The hidden costs of large LLM context windows

Large context windows in models like GPT-5 seem to eliminate the need for RAG, but the high token cost makes this approach expensive and unscalable for every request.

#2about 3 minutes

A brief introduction to vectors and vector search

Text is converted into numerical vector embeddings that capture its semantic meaning, allowing computers to efficiently calculate the similarity between different phrases or documents.

#3about 9 minutes

How to classify text using a vector database

Instead of using a costly LLM for every classification task, you can use a vector database to match new text against pre-embedded reference examples for a specific label.

#4about 5 minutes

Using semantic routing for efficient tool calling

By matching user prompts against pre-defined reference phrases for each tool, you can directly trigger the correct function without an initial, expensive LLM call.

#5about 5 minutes

Reducing latency and cost with semantic caching

Semantic caching stores LLM responses and serves them for new, semantically similar prompts, which avoids re-computation and significantly reduces both cost and latency.

#6about 7 minutes

Strategies for optimizing vector search accuracy

Improve the accuracy of vector search patterns through techniques like self-improvement, a hybrid approach that falls back to an LLM, and chunking complex prompts into smaller clauses.

#7about 3 minutes

Addressing advanced challenges in semantic caching

Mitigate common caching pitfalls, like misinterpreting negative prompts, by using specialized embedding models and combining semantic routing with caching to avoid caching certain types of queries.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Increasing the value of talk recordings post-event
04:57 MIN

Increasing the value of talk recordings post-event

Cat Herding with Lions and Tigers - Christian Heilmann

Prompt injection as an unsolved AI security problem
07:39 MIN

Prompt injection as an unsolved AI security problem

AI in the Open and in Browsers - Tarek Ziadé

The value of progressive enhancement and semantic HTML
03:31 MIN

The value of progressive enhancement and semantic HTML

WeAreDevelopers LIVE – You Don’t Need JavaScript, Modern CSS and More

Unlocking LLM potential with creative prompting techniques
04:59 MIN

Unlocking LLM potential with creative prompting techniques

WeAreDevelopers LIVE – Frontend Inspirations, Web Standards and more

The hardware requirements for running LLMs locally
03:55 MIN

The hardware requirements for running LLMs locally

AI in the Open and in Browsers - Tarek Ziadé

Using AI to overcome challenges in systems programming
02:49 MIN

Using AI to overcome challenges in systems programming

AI in the Open and in Browsers - Tarek Ziadé

Preventing exposed API keys in AI-assisted development
03:45 MIN

Preventing exposed API keys in AI-assisted development

Slopquatting, API Keys, Fun with Fonts, Recruiters vs AI and more - The Best of LIVE 2025 - Part 2

Making accessibility tooling actionable and encouraging
03:58 MIN

Making accessibility tooling actionable and encouraging

Developer Time Is Valuable - Use the Right Tools - Kilian Valkhof

Featured Partners

Related Videos

See all videos
WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more1:05:44

WeAreDevelopers LIVE - Vector Similarity Search Patterns for Efficiency and more

Chris Heilmann, Daniel Cranney, Raphael De Lio & Developer Advocate at Redis

How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge42:26

How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge

Meta Atamel & Guillaume Laforge

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation21:17

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps28:57

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

Dieter Flick & Michel de Ru

Martin O'Hanlon - Make LLMs make sense with GraphRAG24:42

Martin O'Hanlon - Make LLMs make sense with GraphRAG

Martin O'Hanlon

Semantic AI: Why Embeddings Might Matter More Than LLMs26:05

Semantic AI: Why Embeddings Might Matter More Than LLMs

Christian Weyer

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j46:51

Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j

Three years of putting LLMs into Software - Lessons learned26:30

Three years of putting LLMs into Software - Lessons learned

Simon A.T. Jiménez

Related Articles

View all articles
DC
Daniel Cranney
Dev Digest 196: AI Killed DevOps, LLM Political Bias & AI Security
Inside last week’s Dev Digest 196 . ⚖️ Political bias in LLMs 🫣 AI written code causes 1 in 5 security breaches 🖼️ Is there a limit to alternative text on images? 📝 CodeWiki - understand code better 🟨 Long tasks in JavaScript 👻 Scare yourself into n...
Dev Digest 196: AI Killed DevOps, LLM Political Bias & AI Security

From learning to earning

Jobs that call for the skills explored in this talk.