With so much buzz around AI, it's easy to get lost in the noise.
So let's break it down. In every issue, we'll dive into:
the latest AI developments
how they can be practically applied
how they can shape your business both today and in the future
Let’s explore how these AI innovations can help you drive real-world impact.
This Week's Insights
Are Tools Like Langhain and LlamaIndex Ready for Production?
Is OpenAI O1 the right tool for your specific use case?
How to avoid the AI wrapper trap
An exploration of Google's NotebookLM and its capabilities
Understanding the roles of inference, training, and fine-tuning in AI
The future of AI agents in business operations
Investing in data centers and power infrastructure for AI
Nuclear power's role in supporting future AI demands
Highlights from OpenAI DevDay
Leveraging generative AI in Master Data Management (MDM)
🛠️ Are We Bottlenecking Our Path to Production?
Tools like Langhain and LlamaIndex are getting a ton of hype in the world of AI.
But is the space ready for formalizing workflows into tools? Or are we adopting these tools too early and inevitably bottlenecking ourselves on the path to production?
📚 Check out my perspective on tooling in emerging technology here.
🎙️ We also dug deep into this topic in a bonus episode of the Talking AI Podcast.
🤖 Elevating AI with OpenAI o1’s Advanced Reasoning
OpenAI O1 is not for everyone. But is it for you? If so, when?
At the moment, OpenAI O1 seems the brightest star in the Generative AI space. And not without some merit.
Its capabilities around reasoning and reflection push the frontier on what can be done in highly complex, logical fields such as maths and sciences.
But just because it is the right tool for some use cases, doesn't mean it's the right tool for all.
There's a constellation of LLMs, each exerting its own gravitational pull based on how it uniquely balances the factors of speed, cost, performance, window capability, and more.
When you factor in the adoption of different LLM APIs of features such as context caching and structured outputs, this picture becomes more complicated.
Simply, different models offer different value propositions. The model you use for one use case shouldn't be the one you use for another. An agent to assist you with code rewrite should be built on a different base model than the one that answers RAG-based FAQs for end-users.
Multi-model workflows are here to stay–so stargaze then fuel your spaceship 🧑🏻🚀🚀.
🆓 NotebookLM, Here's What You Need to Know
Google's experimental Notebook LM is the latest no-code RAG offering that aims to democratize Q&A on end-user documents. But lurking under the simplicity of the interface are some hairy challenges around data quality and self-service analytics.
NotebookLM is a chat-based interface on top of a Language Manual that allows users to seamlessly upload and converse with documents containing text and images.
There are native integrations into Google Docs and Slides. A single notebook allows uploading 50 sources and selecting an active subset for the LLM to use in its responses. Notebooks support sharing with "Viewer" and "Editor" roles.
Grizzled Data and BI veterans will shudder when they see the issue.
Data risks becoming outdated as soon it's uploaded as NotebookLM creates a static copy of all its data sources. Without the metadata to tell us who uploaded the document, when they did it, nor where the original is, the citation feature loses its value.
As notebooks proliferate, these conflicting sources of information will yield different results, many outdated, and instill doubt in our analytics.
There's a lot to like about NotebookLM: its multimodal, bring-your-own-document RAG with its long context window makes it easy to get insights right away. But when self-service comes without observability, buyers beware.
📰 Your AI News Brief
Stay updated with the latest developments in artificial intelligence.
Salesforce launches Agentforce: While it's uncertain if Salesforce will dominate the AI agent space, one thing is clear—AI agents are here to stay.
⏳ Accelerate AI Development: Grasping Core Concepts
If you are serious about mastering AI, you need to know the difference between inference, training, and fine-tuning.
David Berrio, a Senior AI/ML Engineer at HatchWorks AI, offers a practical breakdown to help you make sense of it.
In this article, David explores how each process plays a role in optimizing AI models for real-world applications, particularly in reducing development cycles and improving performance.
Did you miss our latest HatchWorks AI Lab on Generative AI & MDM Automation?
No problem!
You can catch the full recording, where we explored how Generative AI can be leveraged in the discipline of Master Data Management (MDM).
Learn how AI-driven automation can streamline data workflows, enhance accuracy, and reduce time to value for your business. If you’re focused on data operations or looking to integrate AI into your MDM processes, this is a must-watch!
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