How can AI benefit organizations?

AI provides efficient Knowledge Management solutions.

What are common methods to impart knowledge to large language models?

Fine-tuning models and using in-context learning (RAG).

Accuracy in answering complex questions.

Improving data parsing and enhancing document relevance.

AI's value in Knowledge Management is highlighted by its ability to efficiently handle vast amounts of documentation and meeting notes, potentially challenging traditional search engines.
Implementing Retrieval Augmented Generation (RAG) methods, such as fine-tuning models and in-context learning, is crucial for enhancing real-world Knowledge Management tasks, despite facing challenges like messy data and the need for advanced tactics to improve accuracy.

How can AI benefit organizations?
AI provides efficient Knowledge Management solutions.
What are common methods to impart knowledge to large language models?
Fine-tuning models and using in-context learning (RAG).
What challenges exist in implementing RAG for AI chatbots?
Accuracy in answering complex questions.
How can organizations optimize RAG pipelines for better performance?
Improving data parsing and enhancing document relevance.
What tools can assist in preparing data for large language models?
Llama Parts and Fire Craw.

00:00

AI's significant value lies in Knowledge Management within organizations, handling vast amounts of documentation and meeting notes efficiently.
Large language models can read and process diverse data, providing personalized answers, potentially challenging search engines like Google.
Platforms like Chat GPT and Plexity are increasingly used for day-to-day queries, with Link focusing on corporate Knowledge Management.
Building AI chatbots to interact with documents is feasible, but challenges exist in their ability to answer complex questions accurately.
Two common methods to impart knowledge to large language models are fine-tuning models or using in-context learning, known as Retrieval Augmented Generation (RAG).
Setting up a proper RAG pipeline involves data preparation, converting data into a vector database for semantic understanding, and retrieving relevant information for user queries.
Challenges in RAG implementation include messy real-world data, requiring different retrieval methods for structured and unstructured data.
Real-world Knowledge Management tasks often require advanced RAG tactics to mitigate risks and improve accuracy.
Tools like Llama Parts and Fire Craw assist in preparing messy data from PDFs and websites into a format suitable for large language models.
Optimizing RAG pipelines involves improving data parsing, determining optimal chunk sizes for documents, and enhancing document relevance through techniques like re-ranking and hybrid search.

13:01

Vector search can be enhanced by combining vector search and keyword search results, selecting the most relevant ones.
The focus is on improving the agentic rack, utilizing agents' dynamic and reasoning abilities to optimize the rack line.
Query translation and planning involve modifying user questions to be more retrieval-friendly for better search results.
The step back method, introduced by Google Deep Mind, abstracts questions for more effective searches.
Utilizing metadata filtering and routing can enhance search relevance by combining metadata with agentic behavior.
Self-reflection processes like corrective rack agents aim to deliver high-quality results by evaluating and refining retrieved documents.
Building a corat rack agent involves using llama stre on a local machine and a file crawl for website scripts.
The agent workflow includes retrieving relevant documents, grading their relevance, generating answers, and checking for hallucinations.
The use of conditional edges in the Lang graph allows for routing based on document relevance, hallucination, and answer accuracy.