The dense fog of RAG: navigating dense retrieval's blind spots

Jordan Moshcovitis,

Tirath Ramdas published on 2024-12-20 included in category RAG

Dense retrieval powers RAG systems, but comes with hidden complexities. Learn to identify and address these challenges.

Effective RAG evaluation: integrated metrics are all you need

Jordan Moshcovitis,

Tirath Ramdas published on 2024-12-05 included in category RAG

Retrieval-Augmented Generation (RAG) pipelines have revolutionised how we integrate custom data with large language models (LLMs), unlocking new possibilities in AI applications. However, evaluating the effectiveness of these pipelines has presented a major challenge for most real-world applications. In this post, we’ll look deeper into the pain points of RAG pipeline evaluation and explore strategies to overcome them.

Improved chatbot customer experience: sleep() is all you need

Tirath Ramdas published on 2024-06-29 included in category Chatbots

How chatbots can be made more human-like by incorporating some wisdom on human psychology.

RAG against the infinite context machine: unit economics is all you need

Tirath Ramdas published on 2024-03-13 included in category RAG

Retrieval Augmented Generation is sometimes viewed as a hack, but in practice it offers several important operational benefits.

Reliable RAG: preprocessing is all you need

Jordan Moshcovitis,

Tirath Ramdas published on 2024-02-22 included in category RAG and series

Preprocessing content before vector DB ingest improves information retrieval performance of Retrieval Augmented Generation (RAG) with favourable unit economics. Let's take a look at propositional chunking, an effective semantic preprocessing technique that is practical to implement.

Document 0

Tirath Ramdas published on 2023-12-22 included in category Housekeeping

This site will focus on all aspects of agentic-RAG motivated by the pursuit to create an effective AI co-pilot for research tasks. Please subscribe to be notified when new content is added. Thanks!