Practical Solutions for Large Language Models (LLMs) and Retrieval Augmented Generation (RAG)
Large Language Models (LLMs) Fine-Tuning
LLMs can be fine-tuned using proprietary documents for specific company needs, but this process is computationally intensive and may hinder the model’s ability to generalize to new knowledge.
Retrieval Augmented Generation (RAG)
RAG offers a more adaptable and scalable method for managing substantial document collections. It comprises an LLM, a document database, and an embedding model to preserve semantic information.
Golden Retriever Framework
The Golden Retriever framework improves the question-answering procedure prior to document retrieval by enhancing the user’s input query, particularly in technical fields. It clarifies the meaning of domain-specific jargon and acronyms, ensuring the RAG framework selects the most relevant documents for the user’s query.
Value and Practical Applications
Golden Retriever significantly improves the accuracy and relevance of information retrieved from large industrial knowledge stores, making it a valuable tool for organizations with extensive and specialized knowledge bases.
Key Contributions
The team has acknowledged and tackled the challenges posed by using LLMs to query knowledge bases in practical applications, especially with regard to context interpretation and handling of domain-specific jargon.
An improved version of the RAG framework has been presented, providing a more reliable method for finding pertinent documents, even in situations where the terminology may be unclear or the context may be inadequate.
Golden Retriever has been shown to be significantly more accurate and effective than baseline algorithms at extracting relevant information from large-scale knowledge libraries.
Evolve Your Company with AI
Use Golden Retriever to redefine your way of work, stay competitive, and address legacy code modernization. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to leverage AI for your advantage.
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