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Artwork for The Memriq AI Inference Brief – Leadership Edition

The Memriq AI Inference Brief – Leadership Edition

Keith Bourne
Generative AI
Retrieval Augmented Generation
Retrieval-Augmented Generation
AI Systems
Semantic Caches
Langchain
AI Inference
Graph-Based RAG
Chromadb
RAG
Knowledge Graphs
Vector Databases
AI Application Development
Artificial Intelligence
Customer Support
Financial Services
Healthcare
AI Technology
Keith Bourne
AI Evaluation Metrics

The Memriq AI Inference Brief – Leadership Edition is a weekly panel-style talk show that helps tech leaders, founders, and business decision-makers make sense of AI. Each episode breaks down real-world use cases for generative AI, RAG, and intelligent agents—without the jargon. Hosted by a rotating panel of AI practitioners, we cover strategy, roadmapping, risk, and ROI so you can lead AI initiat... more

PublishesDailyEpisodes25Founded2 months ago
Number of ListenersCategories
BusinessTechnologyManagement

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Artwork for The Memriq AI Inference Brief – Leadership Edition

Latest Episodes

How does AI make smart decisions when it doesn’t have all the facts? In this episode of Memriq Inference Digest - Leadership Edition, we break down belief states—the AI’s way of representing what it knows and, critically, what it doesn’t. Learn why t... more

Unlock the potential of Recursive Language Models (RLMs), a groundbreaking evolution in AI that empowers autonomous, strategic problem-solving beyond traditional language models. In this episode, we explore how RLMs enable AI to think recursively—bre... more

# Evaluating Agentic AI: DeepEval, RAGAS & TruLens Frameworks Compared

In this episode of Memriq Inference Digest - Leadership Edition, we unpack the critical frameworks for evaluating large language models embedded in agentic AI systems. Leaders na... more

Discover how the Model Context Protocol (MCP) is revolutionizing AI system integration by simplifying complex connections between AI models and external tools. This episode breaks down the technical and strategic impact of MCP, its rapid adoption by ... more

Is agent engineering the next big AI discipline or a repackaged buzzword? In this episode, we cut through the hype to explore what agent engineering really means for business leaders navigating AI adoption. From market growth and real-world impact to... more

Is your AI initiative falling short despite the hype? The root cause often lies not in the AI technology itself but in how your architecture handles the Natural Language Understanding (NLU) layer. In this episode, we explore why treating AI as a bolt... more

In this episode of Memriq Inference Digest - Leadership Edition, we explore how Retrieval-Augmented Generation (RAG) systems maintain quality and trust at scale through advanced evaluation methods. Join Morgan, Casey, and special guest Keith Bourne a... more

Semantic caches are revolutionizing AI-powered applications by drastically reducing query latency and inference costs while improving response consistency. In this episode, we unpack Chapter 15 of Keith Bourne’s 'Unlocking Data with Generative AI and... more

Key Facts

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Number of Listeners
Find out how many people listen to this podcast per episode and each month.

Recent Guests

Keith Bourne
Author of 'Unlocking Data with Generative AI and RAG'
Episode: Using LangChain to Get More from RAG (Chapter 11)

Hosts

Casey
Co-host of Memriq Inference Digest, Leadership Edition
Morgan
Host of the Memriq Inference Digest Leadership Edition

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

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#137
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Talking Points

Recent interactions between the hosts and their guests.

Belief States Uncovered: Navigating AI’s Knowledge & Uncertainty
Q: What should businesses consider when adopting belief states for their AI systems?
Businesses should aim for actionable beliefs that improve decision-making and respond appropriately to uncertainty.
Vectors & Vector Stores: Deep Dive into RAG’s Secret Sauce (Chapter 7)
Q: Keith, as the author, what made you prioritize vectors and vector stores in Chapter 7?
Without vectors, the whole retrieval piece of RAG collapses. Embeddings let AI understand context at scale, and vector stores make that usable in production.
Security in RAG Systems (Chapter 5)
Q: How does the book frame this duality of RAG?
The author points out that RAG is both a security opportunity and challenge. It can improve transparency by attaching source documents to answers but also inherits vulnerabilities from both the AI model and the data retrieval layer.
Security in RAG Systems (Chapter 5)
Q: Keith, what's the biggest mistake you see organizations making here?
Overconfidence. Many think the AI is a black box they can't open, so they ignore security until a crisis hits.
Security in RAG Systems (Chapter 5)
Q: What does that look like in practice?
Key tools and approaches include OpenAI, GPT-4o and 3.5 for language understanding, Lang chain frameworks for managing retrieval and generation, Python Dotonvi for secrets management, Giscard's LLM scan for automated red teaming, and Git for version control.

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Offering insights into the world of artificial intelligence, this series centers on helping tech leaders and business decision-makers navigate the complexities of AI initiatives. Episodes typically explore key concepts such as generative AI, Retrieval Augmented Generation (RAG), and intelligent agents, breaking them down into practical, real-world applications. Special guests often share their expertise, contributing to discussions on strategies, risk management, and potential return on investments in AI technologies. Notable for its accessible approach, the show eliminates technical jargon, making it easier for leaders to engage with the intricacies of AI without feeling overwhelmed.

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this podcast launched 2 months ago and published 25 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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