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A fundamental new search experience with PoolParty Semantic Retrieval-Augmented Generation

Looking at the variety of human-machine interactions we have developed over the last 70 years, search engines have some of the greatest impact, as they are the link between the large knowledge repositories we have created and our daily use of them.

However, the way we search and retrieve knowledge is constantly evolving. Just a few years ago, we had to use our own query languages and Boolean abbreviations where typography was as important as word order. While query language has become simpler and closer to natural language in recent years, the burden of retrieving results from lists and short descriptions remains.

And although modern search engines had begun using sophisticated ranking algorithms and faceting options to determine the desired result, we were still a long way from the futuristic ideal from the TV series Star Trek: "Computer! Give me an answer to my question!"

Now we are opening a new chapter in the history of search.

The interplay of semantic search, search assistants, and retrieval-augmented generation is creating a new kind of human-machine interaction with stored knowledge.

Introducing: PoolParty’s Semantic Retrieval Augmented Generation (Semantic RAG)

A fundamental new search experience with Semantic RAG

We all know the time-consuming process of finding an answer or document. To initiate a search, the information requirement must first be broken down into questions and then translated into syntax that works for the query. The lists of results obtained must be viewed, evaluated, summarized and finally reformulated as knowledge that can be used further. Overall, a lengthy cognitive process.

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Semantic RAG now combines three groundbreaking innovations in search: Prompt Engineering, Semantic Search and LLM. Semantic RAG changes both the way we search and the way we receive and evaluate results. Searching for keywords and scrolling through lists of results are now history.

Retrieval Augemented Generation Search Experience

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An AI assistant already assists in formulating the question. Autocomplete and concept suggestions help to formulate the question in a targeted and domain-specific way. Thanks to the additional semantic information provided, the immediately generated answer delivers results that reflect the context and search intent.

The engine’s ability to further deepen the search makes it possible to understand the knowledge offered and at the same time to concentrate the question more on the desired information they seek.

Finally, a recommendation algorithm identifies documents in the database or drive that best match the result of the human-machine dialog and returns them as a final conclusion.

Each step along the way produces additional links for research and knowledge enhancement.

How Semantic RAG impacts your work

Time to insight

A combination of AI assistants ensures that processable knowledge is presented immediately after submitting the query. Where conventional searches can only offer lists of results, Semantic RAG already provides summarized facts.

Relevancy and trust

The semantic core of the search engine ensures that results remain relevant in relation to the user’s company or domain context. The underlying knowledge model ensures higher transparency and traceability as well as a minimum of AI artifacts. Users can access the model's sources, which promotes trust in the content and lets users verify its accuracy.

Savvy querying for the untrained

The LLM assistant picks up employees at their current level of knowledge and enables on-the-fly exploration and learning. Untrained personnel can begin using and accessing knowledge from the company drive regardless of their technical language or how precise they are with their queries.

An AI search landscape that works for you!

A Semantic RAG is the best choice if you want to harness the developments of Generative AI for your company. It uses AI technologies where assistants can be used to simplify and shorten processes and it relies on semantic technologies where fact accuracy and machine training are required.

Trained by an existing knowledge model used by a company
Tailored to the organization’s jargon and naming conventions
Universal connector to other systems

Would you like to use this new search experience for your company?

What we have demonstrated here is also possible with your company's own knowledge base. We develop GenAI-supported search, recommendation and assistants for you.

Are you looking for IT tools for your ESG strategy?

This demonstrator is part of our toolbox that can support you in implementing your ESG Staretgy with software solutions.