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4 posts tagged with "rag"

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Ā· 9 min read
Fernando Guerra
Fotis Nikolaidis

RAG is a groundbreaking approach that combines the strengths of information retrieval (IR) techniques with the creative capabilities of LLMs. This improvement transforms LLMs from being merely conversationalists to experts capable of engaging in in-depth and contextually rich dialogues on specialized topics, significantly enhancing their use and applicability across various domains.

Large Language Models (LLMs) are typically trained to converse on a wide range of topics with relative ease. However, their responses often lack depth and specificity and they might struggle to engage in detailed discussions on specialized subjects due to a lack of domain-specific knowledge. To overcome this, RAG fetches relevant information from different data sources in real-time and incorporates it into its responses; With it, the RAG model acts as an expert that evolves a general LLM into a specialized one, capable of retrieving and utilizing relevant information to provide precise responses, even to queries that require knowledge beyond its initial training data.

RAG Image

Ā· 9 min read
Anita Okoh
Fernando Guerra

Querying your SQL database purely in human languageā€‹

RAG = DuckDB + SuperDuperDB + Jina AI

Unless you live under a rock, you must have heard the buzzword ā€œLLMsā€.

Itā€™s the talk around town.

LLM models, as we all know, have so much potential. But they have the issue of hallucinating and a knowledge cut-off.

The need to mitigate these two significant issues when using LLMs has led to the rise of RAGs and the implementation of RAGs in your existing database.

Ā· 4 min read
Duncan Blythe

Despite the huge surge in popularity in building AI applications with LLMs and vector search, we haven't seen any walkthroughs boil this down to a super-simple, few-command process. With SuperDuperDB together with MongoDB Atlas, it's easier and more flexible than ever before.


We have built and deployed an AI chatbot for questioning technical documentation to showcase how efficiently and flexibly you can build end-to-end Gen-AI applications on top of MongoDB with SuperDuperDB:

Implementing a (RAG) chat application like a question-your-documents service can be a tedious and complex process. There are several steps involved in doing this:

Ā· 9 min read
Nick Byrne

Imagine effortlessly infusing AI into your data repositoriesā€”databases, data warehouses, or data lakesā€”without breaking a sweat. With SuperDuperDB, we aim to make this dream a reality. We want to provide everyone with the tools to build AI applications directly on top of their data stores, with just a pinch of Python magic sprinkled on top! šŸāœØ

In this latest blog post we take a dive into one such example - a Retrieval Augmented Generation (RAG) app we built directly on top of our MongoDB store.