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Context engineering vs. prompt engineering
Learn how context engineering and prompt engineering differ and why mastering both is essential for building production AI agents and RAG systems.
Faster ES|QL stats with Swiss-style hash tables
How Swiss-inspired hashing and SIMD-friendly design deliver consistent, measurable speedups in Elasticsearch Query Language (ES|QL).
Managing agentic memory with Elasticsearch
Creating more context-aware and efficient agents by managing memories using Elasticsearch.
Higher throughput and lower latency: Elastic Cloud Serverless on AWS gets a significant performance boost
We've upgraded the AWS infrastructure for Elasticsearch Serverless to newer, faster hardware. Learn how this massive performance boost delivers faster queries, better scaling, and lower costs.
jina-embeddings-v3 is now available on Elastic Inference Service
Introducing jina-embeddings-v3 on Elastic Inference Service (EIS) and explaining how to get started.
Hybrid search and multistage retrieval in ES|QL
Explore the multistage retrieval capabilities of ES|QL, using FORK and FUSE commands to integrate hybrid search with semantic reranking and native LLM completions.
Implementing an agentic reference architecture with Elastic Agent Builder and MCP
Explore an agentic reference architecture with Elastic Agent Builder, MCP, and semantic search to build a security agent for automated threat analysis.
Automating log parsing in Streams with ML
Learn how a hybrid ML approach achieved 94% log parsing and 91% log partitioning accuracy through automation experiments with log format fingerprinting in Streams.
An introduction to Jina models, their functionality, and uses in Elasticsearch
Explore Jina multimodal embeddings, Reranker v3, and semantic embedding models, and how to use them natively in Elasticsearch.
How to build an agent knowledge base with LangChain and Elasticsearch
Learn how to build an agent knowledge base and test its ability to query sources of information based on context, use WebSearch for out-of-scope queries, and refine recommendations based on user intention.
NeurIPS 2025 highlights: From model merging to deep learning for code
Explore our NeurIPS 2025 highlights on model merging, task vectors, and VLM dynamics, plus our DL4C workshop presentation on Jina code embeddings.
