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Boosting LLMs Performance with Retrieval-Augmented Generation (RAG)

Agenda

Foundation models are typically trained offline, which makes them frozen in time and unaware of the current data. They may also lack effectiveness in domain-specific tasks due to their general training. Retrieval Augmented Generation (RAG) enhances prompts by retrieving external data from various sources like documents, databases, or APIs. This involves converting data into numerical representations using embedding language models. RAG then appends relevant context from the knowledge base to the user’s prompt, improving model performance. Knowledge libraries can be updated asynchronously to keep the information current.

Key Takeaways:
– RAG methods enhance model performance by incorporating external data into prompts.
– RAG can be personalized for specialized domains like medicine, law, and many more.
– External data sources can include documents, databases, or APIs.
– Knowledge libraries can be updated independently to keep information current.

Umar Jawad-LLMs-Generative AI-RAG
Umar Jawad

Data Scientist at Vaisala

Umar Jawad is a data scientist specializing in GenAI use cases and solutions with a focus on the open-source ecosystem. With a Master’s Degree in Data Analytics from Tampere University in Finland, and expertise in ML, AI, Statistics, Data Mining, and Graph networks, he aims to bridge the gap between business and tech. Umar is proficient in Python, R, and AWS cloud, and experienced with tools like Jira, Confluence, and Bitbucket. 

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