LLM

Large language models (LLMs) have become essential tools in various industries. While we previously discussed how to select the best LLM for your needs, we haven’t yet explored how to optimize LLMs for cost-efficient, production-ready setups. While there are a host of options for optimization, in this article, we’re going to explore options for optimizing LLMs including the usage of Open-Source, hardware selection as a strategy, and experiments driving our understanding of the benefits of modifying precision values. 

Struggling to create buyer personas? Discover how non-tech professionals can leverage AI to streamline the process. This post explores using ChatGPT to generate detailed personas and craft effective outreach messages. Learn how to optimize prompts and refine your approach for optimal results.

Our last post comparing Claude Opus vs GPT 4 highlighted Claude’s capabilities in areas such as summarization, spatial understanding, human-like writing style, and advanced knowledge.

Using LLMs to Create API Calls. Proxet answers the call to experiment - should data engineers be considered with this new use case for LLMs?

Enhance LLM accuracy with RAG: A cost-effective method for reliable outcomes. Building and evaluating a RAG-based solution is key.

How LLMs can help us solve the very errors in document understanding that they initially induce

Accurate parsing enables Q&A quality — but is it possible? No matter the industry or sector, businesses regularly deal with the question of how to efficiently process large amounts of info-heavy documents. Organization leaders, including CTOs, CDOs, and CPOs, are often looking for solutions to this question.

Dive deep into the technicalities of embedding models, vector databases, and optimization strategies to revolutionize information retrieval