Accelerating generative AI experiences

Daily breakthroughs in large language models (LLMs) and generative AI have put developers at the forefront of the movement, influencing its direction and possibilities. In this blog, I’ll share how Elastic's search customers are using Elastic's vector database and open platform for search powered AI and developer tools to accelerate and scale generative AI experiences, giving them new avenues for growth. 

Results from a recent developer survey conducted by Dimensional Research and supported by Elastic indicate that 87% of developers already have a use case for generative AI — whether it’s data analysis, customer support, workplace search, or chatbots. But only 11% have successfully implemented these use cases into production environments. 

There are several factors getting in their way:

• Model deployment and management: Choosing the right model requires experimentation and rapid iteration. Deploying LLMs for generative AI applications is time-consuming and complex with a steep learning curve for many organizations.

• Legal and compliance concerns: These concerns are especially important when dealing with sensitive data and can be a barrier to model adoption.

• Scaling: Domain specific data is crucial for LLMs to understand context and generate accurate outputs. Retrieving that as your data scales requires equally scalable support for the workloads that generate vector embeddings, increasing the demand for memory and computational resources rapidly. With vast data sets, context windows are large and costly to pass to an LLM, and more context does not necessarily mean more relevance. Only a robust platform of tools can shape the context and balance the tradeoffs between relevance and scale to achieve a viable future proof architecture for innovation.

Developers seek a reliable, scalable, and cost-effective way to build generative AI applications and a platform that simplifies implementation and the LLM selection process.

Elastic is consistently delivering solutions to these developer concerns with a rapid pace of innovation to support generative AI use cases.

To quickly implement and scale RAG workloads, the Elastic Learned Sparse EncodeR (ELSER) — generally available — is an easy to deploy, optimized, late interaction machine learning (ML) model for semantic search. ELSER delivers contextually relevant search results without requiring fine tuning and offers developers a built-in trusted solution, saving you the time and complexity of model selection, deployment, and management. 

ELSER elevates search relevance without a cost to speed — when Consensus upgraded its academic research platform powered by Elastic, using ELSER, it cut search latency by 75% with improved accuracy. 

When you pair ELSER with the E5 embedding model, you can easily apply multilingual vector search. Our optimized artifact of E5 is tailored specifically for Elasticsearch deployments. Multilingual search is also available by uploading multilingual models or integrating with Elastic’s Inference API (for example, Cohere's multilingual model embeddings). These advancements accelerate retrieval augmented generation (RAG) further, making Elastic critical infrastructure for scaling the innovative generative AI experiences you build.

Elastic is also focused on scaling these experiences efficiently. Scalar quantization, which came with our 8.12 release, is a game-changer for vector storage. Large vector expansions can lead to slower searches. But this compression technique dramatically slashes memory requirements by fourfold and helps pack in more vectors, and at higher scales, has a negligible impact on recall. It doubles vector search speeds used in RAG without sacrificing accuracy. The result? A leaner, faster system that trims infrastructure costs at scale.

Relevance is the key to the best generative AI experiences. Using ELSER for semantic search and BM25 for textual search are excellent first steps for retrieving relevant documents as context for LLMs. Large context windows can be further refined using reranking tools that are now part of the Elastic Stack. Rerankers apply powerful ML models to fine-tune your search results and bring the most relevant results to the top based on user preferences and signals. Learning to Rank (LTR) is also now native to the Elasticsearch Platform. This is powerful for RAG use cases, which rely on feeding the most relevant results to an LLM as context. 

Implementation is further simplified through the Inference API and third-party providers like Cohere. Upgrade to our latest release to test the impact that rerankers can have on relevance. 

These approaches not only enhance search accuracy (by 30%, in the case of Consensus), but also help you achieve quick results, refining relevance for RAG and efficiently managing ML workstreams.

Developers can also host diverse transformer models, including public and private Hugging Face models. While Elasticsearch serves as a versatile vector database for the entire ecosystem, developers who prefer tools like LangChain and LlamaIndex can use our integrations to quickly spin up production-ready generative AI apps using LangChain Templates. Elastic's open platform sets you up to quickly adapt, experiment, and accelerate generative AI projects. Elastic was also recently added as a third party vector database for On Your Data, a new service to build conversational copilots. Another good example is Elastic’s collaboration with the Cohere team behind the scenes to make Elastic a great vector database for Cohere embeddings. 

Generative AI is reshaping every organization, and Elastic is here to support the transformation. For developers, the keys to successful generative AI implementations are continuous learning (have you seen Elastic Search Labs yet?) and rapidly adapting to the changing AI landscape.

• Read about these capabilities and more in the Elastic Search release notes.
• Existing Elastic Cloud customers can access many of these features directly from the Elastic Cloud console. Not using Elastic Cloud? Start a free trial.
• Try the Elasticsearch Relevance Engine, our suite of developer tools for building AI search apps.