When looking to create or improve performance on existing Retrieval Augmented Generation or AI Question Answering systems it’s important to understand the characteristics of the problem. Six areas that we look at are:

With robust and rapidly evolving model updates from extremely well funded startups like OpenAI, Anthropic, Cohere already in the market, as well as offerings like Bard from Google, it’s worth taking a step back to understand why the introduction of an open source model like Llama 2 is making such a splash in the online conversation. First, we should state the obvious that we sometimes forget: Despite the rapid improvements and commercialization of LLM models, there’s still some critical issues blocking or limiting success some important use cases.

One of the hot topics in the ecosystem has been the question “Will AI replace human jobs”? A lot of the discussion on the topic centers around the capabilities of the AI (ie can AI write marketing copy, can AI write emails etc), but I think the more important question is on HOW AI works. When I think about how AI works in this context, I don’t mean the guts of the LLM or diffusion model, but how in practice a process is applied the uses AI to create adequate or exceptional results (depending on what is needed). At Gen AI Partners we look at 4 models of human/AI interaction when designing systems. I’ll describe those models and then revisit where AI will augment humans and where it may replace human tasks.

Using LLMs for market feedback can save time, money and help you drive deeper understanding of potential consumers early in your product or idea lifecycle to avoid costly mistakes

Want to up your AI image creation ability? Use ChatGPT and Midjourney in tandem to create better AI generated images. This approach works in the UI or the API.

The emergence of large language models (LLMs) such as GPT/ChatGPT has revolutionized the way we process information. However, myths surrounding the use of LLMs in applications can discourage people from fully utilizing their capabilities. It's important to distinguish between limitations of the applications that can be built with LLMs versus the properties of the models themselves. In this blog post, we will debunk three of the most common myths surrounding LLM applications and show you the incredible potential they offer.

GPT4 Predicts the entire NCAA Tournament Bracket including Key Upsets:

MIDWEST - First Round:

11. Mississippi State over No. 6 Iowa State (72-68) - Assuming Mississippi State wins the play-in game against Pitt.

EAST - Second Round:

7. Michigan State over No. 2 Marquette (70-66)

The highly anticipated GPT-4 has been released by OpenAI, and it brings a range of new capabilities, performance improvements, and pricing implications. In this blog post, we will dive into the key features of GPT-4, the pricing structure, and how you can get access to this powerful language model.

Learn HOW to build an AI powered SMS ChatBot and WHY AI experiences can improve customer engagement.

Right now, some brands may be wary of the negative press on inaccuracies and unpredictable responses from ChatGPT, but we’ll talk about how this can be managed through the AI stack and configuration settings. As brands realize this, we expect this to be one trend that accelerates over the next year…

Like much of the software world, we were captivated by the potential capabilities enabled by the rapid advances in AI research and commercial tools. Complex user interaction systems that only a few years ago would have been aspirational product goals or major investments can now be delivered with only hours or weeks of development. However, the rapid pace of innovation and development can be as challenging as it is exciting. We are here to help with these challenges by marrying a constant study of the ecosystem, with decades of experience on building analytics and data intensive software.

To give a more detail on the expansion of the ecosystem…