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How to Use Generative AI and LLMs to Improve Search

TechEmpower SaaS

Artificial Intelligence (AI), and particularly Large Language Models (LLMs), have significantly transformed the search engine as we’ve known it. With Generative AI and LLMs, new avenues for improving operational efficiency and user satisfaction are emerging every day.

AI Search 519
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The AI API : The Twilio Moment for Machine Learning

Tom Tunguz

Training, deploying, & optimizing machine learning models has historically required teams of dedicated researchers, production engineers, data collection & labeling teams. Even fully staffed, teams required years to develop models with reasonably accurate performance.

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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. Without that nuance labeled and incorporated into the training data, it’s hard for the model to strike the right tone. I’m wondering whether small errors in the first model compound in the second model.

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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. Analytics surface the questions users ask when using the model.

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How Banks Are Winning with AI and Automated Machine Learning

Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Brought to you by Data Robot.

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Building Resolution Bot: How to apply machine learning in product development

Intercom, Inc.

We are at the start of a revolution in customer communication, powered by machine learning and artificial intelligence. So, modern machine learning opens up vast possibilities – but how do you harness this technology to make an actual customer-facing product? The cupcake approach to building bots.

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5 Key Learnings from How Top SaaS Companies are Actually Productizing AI with Paragon

SaaStr

Why LLM Wrappers Failed – And What Works Instead The first wave of AI products were mostly “LLM wrappers” – simple chatbots built on top of models like GPT. Here’s what Brandon Fu (CEO, Paragon) and Ethan Lee (Director of Product) shared at SaaStr AI Day about what’s actually working: 1.

AI 247
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How Banks Are Winning with AI and Automated Machine Learning

Today, banks realize that data science can significantly speed up these decisions with accurate and targeted predictive analytics. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Brought to you by Data Robot.

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5 Things a Data Scientist Can Do to Stay Current

Demand for data scientists is surging. 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. Collecting and accessing data from outside sources.

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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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Data Science Fails: Building AI You Can Trust

The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology. Download the report to gain insights including: How to watch for bias in AI.

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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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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. Holding onto old BI technology while everything else moves forward is holding back organizations.

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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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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.