Category Archives: Uncategorized

Techniques, Challenges, and Future of Augmented Language Models

After attending several conferences in the past month, it’s evident that Retrieval Augmented Generation (RAG) has emerged as one of the most popular techniques in AI over the past year, widely adopted by many AI teams. RAG refers to the process of supplementing a large language model (LLM) with additional information retrieved from elsewhere toContinue reading “Techniques, Challenges, and Future of Augmented Language Models”

Best Practices in Retrieval Augmented Generation

Subscribe • Previous Issues Techniques, Challenges, and Future of Augmented Language Models After attending several conferences in the past month, it’s evident that Retrieval Augmented Generation (RAG) has emerged as one of the most popular techniques in AI over the past year, widely adopted by many AI teams. RAG refers to the process of supplementing a largeContinue reading “Best Practices in Retrieval Augmented Generation”

From Proprietary to Open Source to Fleets of Custom LLMs

Large language models (LLMs) have proven to be powerful tools for me over the last year. LLMs can be used to build a wide range of applications, from chatbots and content generators to coding assistants and question answering systems. After discussing the journey of building applications with friends, I want to share the typical progressionContinue reading “From Proprietary to Open Source to Fleets of Custom LLMs”

Open Source Principles in Foundation Models

The launch of Mistral 7B prompted me to reflect on the concept of open source in relation to Large language Models (LLMs). In essence, an open source LLM is a model whose code is publicly available under an open source license, allowing anyone to use, modify, and distribute the model. Open source machine learning modelsContinue reading “Open Source Principles in Foundation Models”

A Comprehensive Approach to Using LLMs

Subscribe • Previous Issues From Proprietary to Open Source to Fleets of Custom LLMs Large language models (LLMs) have proven to be powerful tools for me over the last year. LLMs can be used to build a wide range of applications, from chatbots and content generators to coding assistants and question answering systems. After discussing the journeyContinue reading “A Comprehensive Approach to Using LLMs”

Efficient Learning with Distilling Step-by-Step

In an era where data is abundant yet precious, a new technique (“Distilling Step-by-Step”)  transforms Large Language Models (LLMs) from mere label predictors to reasoning agents that provide intermediate rationales, bridging the gap between inputs and final answers. This mechanism enables the crafting of efficient task-specific models that require less data, less computational cost, andContinue reading “Efficient Learning with Distilling Step-by-Step”

Keys to a Robust Fleet of Custom LLMs

The rising popularity of Generative AI is driving companies to adopt custom large language models (LLMs) to address concerns about intellectual property, and data security and privacy. Custom LLMs can safeguard proprietary data while also meeting specific needs, delivering enhanced performance and accuracy for improved user experiences and operations. Tailoring these models to specific requirementsContinue reading “Keys to a Robust Fleet of Custom LLMs”

7 Must-Have Features for Crafting Custom LLMs

Subscribe • Previous Issues Keys to a Robust Fleet of Custom LLMs The rising popularity of Generative AI is driving companies to adopt custom large language models (LLMs) to address concerns about intellectual property, and data security and privacy. Custom LLMs can safeguard proprietary data while also meeting specific needs, delivering enhanced performance and accuracy for improvedContinue reading “7 Must-Have Features for Crafting Custom LLMs”