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The Promise and Pitfalls of Chaining Large Language Models for Email

Tom Tunguz

Over the last few weeks I’ve been experimenting with chaining together large language models. Bad data from the transcription -> inaccurate prompt to the LLM -> incorrect output. Tn machine learning systems, achieving an 80% solution is pretty rapid. I dictate emails & blog posts often.

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Which Increases Productivity More : The Advent of Personal Computer or a Large-Language Model?

Tom Tunguz

” That’s the conclusion from OpenAI’s recent paper “ GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. ” How much might US GDP grow assuming large-language models enable US workers to do more? The BEA estimates US GDP is $26.2t.

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Context.ai - Unlocking Insight into LLM-Based Applications

Tom Tunguz

Large-language models have transformed how millions interact with products : from customer support to code generation to legal document analysis. These new engagement models invite users through a meaningfully different product journey. is a product analytics platform for LLM-powered applications. Context.ai

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How to Improve Your LLM : Combine Evaluations with Analytics

Tom Tunguz

The future of LLM evaluations resembles software testing more than benchmarks. Real-world testing looks like this , asking LLMs to produce Dad jokes like this zinger : I’m reading a book about gravity & it’s impossible to put down. LLMs are tricky. 1 can be greater than 4. This is called non-determinism.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the whitepaper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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What are large language models — and how are they used in generative AI?

IT World

When ChatGPT arrived in November 2022, it made mainstream the idea that generative artificial intelligence (AI) could be used by companies and consumers to automate tasks, help with creative ideas, and even code software. Want some ideas for a new marketing or ad campaign? Generative AI to the rescue.

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Machine Learning vs. Artificial Intelligence: What’s The Difference? 

SaaS Metrics

Have you ever mixed-up artificial intelligence and machine learning? Artificial intelligence—often called AI—is the big idea. Machine learning, or ML for short, is just one tool inside. Read more The post Machine Learning vs. ML helps computers. You’re not alone.

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How Banks Are Winning with AI and Automated Machine Learning

By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.

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Resilient Machine Learning with MLOps

Today’s economy is under pressure from inflation, rising interest rates, and disruptions in the global supply chain. As a result, many organizations are seeking new ways to overcome challenges — to be agile and rapidly respond to constant change. We do not know what the future holds.

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Intelligent Process Automation: Boosting Bots with AI and Machine Learning

But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In Data Robot's new ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning, we cover important issues related to IPA, including: What is RPA?

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MLOps 101: The Foundation for Your AI Strategy

Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?

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Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

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The Role of Artificial Intelligence in Pandemic Response: Lessons Learned From COVID-19

In March 2020, the world was hit with an unprecedented crisis when the COVID-19 pandemic struck. As the disease tragically took more and more lives, policymakers were confronted with widely divergent predictions of how many more lives might be lost and the best ways to protect people.

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5 Things a Data Scientist Can Do to Stay Current

With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Fostering collaboration between DevOps and machine learning operations (MLOps) teams.

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How to Choose an AI Vendor

You know you want to invest in artificial intelligence (AI) and machine learning to take full advantage of the wealth of available data at your fingertips. But rapid change, vendor churn, hype and jargon make it increasingly difficult to choose an AI vendor.