DiligentIQ’s latest research explores the strengths and limitations of standalone Large Language Models (LLMs) like ChatGPT and Claude compared to our integrated platform. Our findings reveal a clear advantage: DiligentIQ delivers superior accuracy, relevance, and thoroughness, enabling businesses to access comprehensive insights from extensive datasets. For organizations seeking efficiency and precision, DiligentIQ stands out as an invaluable tool, complementing standalone LLMs for a well-rounded AI strategy.

DiligentIQ's new Agent Mode is our latest step in making Large Language Models (LLMs) work better for you. The Agent Mode feature adds an intelligent decision-making layer to query processing. At its core, Agent Mode acts as an intermediary, refining user queries and determining the most effective path for information retrieval and analysis.

In the expanding world of artificial intelligence, prompt engineering has become an important tool to obtain the best output. Prompt engineering is the process of optimizing prompts to improve the quality, accuracy, and relevance of responses. Large language models (LLMs) are sensitive to minor changes in prompts, proving that prompt engineering can significantly enhance responses. Drawing from our own experience and research with prompt engineering, we aim to provide insights on how effectively refined AI prompts can significantly improve data analysis. This will ultimately have profound implications within the private equity landscape, where precise and insightful analysis is critical for the due diligence process.

In the fast-paced world of AI, it's crucial to grasp how our tools are evolving. Last month AI researchers, Lila Karl and Michael Van Demark, conducted a comprehensive analysis comparing the May and August versions of the ChatGPT-4 Omni model. Their findings reveal both significant improvements and ongoing challenges that are crucial for anyone looking to use AI to drive insights.

At DiligentIQ, we are continually pushing the boundaries of data visualization and chart analysis through the integration of Generative AI. Spearheaded by Software Engineer, Michael Scoleri, our recent efforts have focused on addressing the challenges of accurately reading and interpreting charts using Large Language Models. Michael's work has advanced our understanding and capability in this area, and I'm pleased to share the insights and progress we've made.

In the ever-evolving world of data analytics, the ability to create effective and insightful visualizations is crucial. At DiligentIQ, we leverage the Assistant API from OpenAI to enhance our Data Chat capabilities, making it easier than ever to generate high-quality charts and visualizations from datasets in xlxs files. Join us in exploring how to successfully harness the power of Generative AI to transform data visualization.

2023 was a wild year in the world of Generative AI. It also has so much potential to dramatically impact business workflows (not to mention personal creativity) that with a strong nudge from Matthew Brandman, I came out of retirement and started DiligentIQ. We put together a team with deep PE and Credit industry knowledge and amazing technical engineering skills.

As we ramp up our pilot clients at DiligentIQ, we are learning a lot about the art of constructing system prompts

Stay in the loop with this curated collection of articles, developments, announcements, and tutorials.

Here we go again …

I feel like a kid in a candy store with $20 circa 1980s!

Here are some cool custom instructions if you’re playing with chatGPT.

This guidance from Anthropic for their top end model is fascinating: Adding the sentence “Here is the most relevant sentence in the context:” to the start of Claude’s response was enough to raise Claude 2.1’s score from 27% to 98% on the original evaluation.‍