About knowledge-hub.eco

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.

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

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.