Playground is a low code interface for developers to explore grounding LLMs of their choice with their own private data, in minutes.

What is Playground?

A/B test LLMs and choose different inference providers<a id="ab-test-llms-and-choose-different-inference-providers"></a>

The best context window with retrievers and hybrid search<a id="the-best-context-window-with-retrievers-and-hybrid-search"></a>

Using the playground

Connect to your LLM

Chat with your data

Indexing a PDF document with BM25 with Python

Chatting with your data with Playground

What happens behind the scenes?

Asking a question

Asking a follow up question

Context

Improving retrieval with Semantic Search and Chunking

Sparse Vector Semantic search with ELSER

Chunking and ingesting the PDF with pyPDF and LangChain

Creating an ELSER Inference Endpoint

Ingesting into Elasticsearch

Splitting pages into passages and ingesting into Elasticsearch

See it in action on Playground

Hybrid Search made simple

Improve the LLM’s answers<a id="improve-the-llms-answers"></a>

System Prompt<a id="system-prompt"></a>

Optimizing Context<a id="optimizing-context"></a>

Final Step! Integrate RAG into your application

Conclusion

Build, test, fun with playground

Playground: Experiment with RAG applications with Elasticsearch in minutes

Learn about Retrieval Augmented Generation (RAG) and how it can help improve the quality of an LLM's generated responses by providing relevant source knowledge as context.

Challenges with Generative AI

Lack of domain knowledge

Frozen parametric knowledge

Hallucinations

Expensive to train

How does RAG help solve this problem?

How does RAG work?

RAG pipeline flow

Retrieval of source knowledge

Advantages of RAG

Grounded in source knowledge

Citing sources in responses

RAG in action

Build a context

Prompt template

Question

Challenges with RAG

Precise dense vectors

Large context

A RAG pipeline typically has the following main components: 1) Data: A collection of data (e.g documents, webpages) that contain relevant information to answer questions. 2) Retrieval: A retrieval strategy that can retrieve relevant source knowledge from the data. 3) Generation: With the relevant source knowledge, generate a response with the help of an LLM.

How does Retrieval Augmented Generation (RAG) work?

Retrieval Augmented Generation: Refine LLM Responses with RAG

Retrieval Augmented Generation (RAG)

Author

Joe McElroy

Articles

Playground: Experiment with RAG applications with Elasticsearch in minutes

Retrieval Augmented Generation (RAG)