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Ep12: What is RAG + Case for Conferences
Manage episode 437142917 series 3579839
Content provided by jmhreif. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by jmhreif or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
This week, we discuss retrieval augmented generation (RAG) and how it’s being used to improve responses (or prevent hallucinations) from an LLM. We also chat about an article on how tech conferences have changed and how you can still find value in speaking or attending, plus how to justify the benefits at your organization.
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Manage episode 437142917 series 3579839
Content provided by jmhreif. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by jmhreif or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
This week, we discuss retrieval augmented generation (RAG) and how it’s being used to improve responses (or prevent hallucinations) from an LLM. We also chat about an article on how tech conferences have changed and how you can still find value in speaking or attending, plus how to justify the benefits at your organization.
51 episodes
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1 Ep51: Exploring AI Agents + Agentic GraphRAG in Java 8:39
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Play Later Play Later Lists Like LikedIn this episode, I delve into the world of agents, discussing my experience with Spring AI tool calling. I share my approach to vector search and graph retrieval tools, address JSON deserialization, and avoid manual boilerplate - the code of which is all available in a Github repository branch . Plus, 1.0 updates to the main branch of the repository using traditional/manual GraphRAG. I wrap up with a recent content piece by Christoffer Bergman from Neo4j, which explores agentic AI frameworks with Java and Neo4j and the differences between traditional and agentic GraphRAG approaches. P.S. Don't forget to leave your feedback/suggestions for BTT in this form ! 00:00 Introduction to Breaktime Tech Talks 00:54 Exploring Spring AI Tool Calling 01:20 Understanding Agentic Frameworks 02:13 Hands-On with Vector Search and Graph Retrieval 02:36 Challenges and Solutions in Tool Functionality 04:02 Updates and Future Plans 05:01 Agentic AI with Java and Neo4j 08:06 Conclusion and Recap…
1 Ep50: GraphRAG with Advisors + Spring AI Concepts 9:50
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Play Later Play Later Lists Like LikedIt's the 50th episode of Breaktime Tech Talks! And to celebrate, I launched a podcast feedback form for you, my listeners. In this 50th episode, follow my latest explorations into Spring AI and GraphRAG. I delve into my attempts to streamline the manual GraphRAG process using Spring AI advisors and tools, sharing the challenges I'm facing, specifically with context parsing from one advisor to the next. I also update the Spring AI starter kit to the 1.0 GA release and recap my Neo4j developer certification livestream . To wrap up, I highlight the Spring AI documentation's AI Concepts page that beautifully blends a blog-post style with key project information.…
1 Ep49: Moving toward AI Tools/Agents + Local RAG app with Quarkus 9:41
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Play Later Play Later Lists Like LikedThis week, I simplified my Langchain4j project with improved prompt variable injection. Then hear my perspective on the role of tools vs. agents in AI workflows—looking at how structured processes differ from autonomous systems, especially in the context of Java frameworks and GraphRAG. Get an inside scoop on how I use different AI coding tools: IntelliJ IDEA for in-flow coding, VS Code with agent mode for problem-solving, and ChatGPT for summarizing and refining content. Lastly, hear highlights from an article on building a local RAG app with Quarkus —clear diagrams and step-by-step breakdown of ingestion vs. retrieval workflows.…
1 Ep48: Java GraphRAG and Langchain4j 1.0 + Thoughts on conversational interfaces 12:08
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Play Later Play Later Lists Like LikedThis week, there were quite a few things I learned: Common steps for implementing GraphRAG in Java using Spring AI and Langchain4j, highlighting key differences in setup and customization. Study prep updates and help on the Neo4j Developer Certification for June! Celebrate Langchain4j’s 1.0 release . Two thought-provoking articles—one on enhancing RAG with graphs , and another analyzing the effectiveness of voice-based interfaces . For a high-level review of steps for GraphRAG in Java, upcoming step-by-step help for prepping to take the Neo4j certification, Langchain4j GA news, and keeping up on tech content, this episode has you covered!…
1 Ep47: Langchain4j GraphRAG Friction + MCP Authorization Headaches 7:38
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Play Later Play Later Lists Like LikedIn this episode, I share some hands-on insights from building apps with Langchain4j using Quarkus and Neo4j , and compare it with Spring AI—especially around how each framework handles vector search and GraphRAG workflows. Spoiler: customization in Langchain4j feels a bit clunky. I also dig into one article's critical take on the MCP authorization spec and why its current approach to security is misaligned with how enterprises actually structure identity and access. The article I discuss breaks down both the architectural intentions and the practical enterprise concerns—token handling, overhead, and developer friction. If you’re working at the intersection of GenAI infrastructure and enterprise systems, this one’s for you.…
1 Ep46: Quarkus Neo4j Application + Development Lessons Learned 9:21
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Play Later Play Later Lists Like LikedIn this episode, we dive into the Quarkus framework with a code repository and an article about development lessons learned. Topics covered include: 🔗 Building a starter application with Quarkus Neo4j and the Object Graph Mapper (OGM). 📝 Exploring similarities and differences between Quarkus and Spring frameworks. 📑 Resources for building with Quarkus and Neo4j - blog post and documentation . 📚 Key takeaways from an article on developer philosophy , touching on code rewrites, estimation challenges, and the importance of automation and edge cases. Whether just curious or writing code, we all learn and face similar development challenges!…
1 Ep45: Exploring Java Frameworks + Launch of Spring AI 1.0 10:03
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Play Later Play Later Lists Like LikedIn this episode, I have some exciting technical updates, along with insights from my recent work and learning. Topics covered include: 📝 Neo4j Java Driver & Object Mapping – My latest blog post and upcoming updates to the GraphAcademy Java courses. 🧪 Framework-less Java Apps – Experiments in building Java applications without a framework and comparing with tools like Spring and others. 🔧 Code Refactoring Strategy – Lessons learned on managing updates in stages for cleaner version control and project maintenance. 🤖 Spring AI 1.0 Release – Highlights from the official launch , including an AWS blog post on architecture insights, real-world examples, and key resources for getting started with AI in Java. Whether you're deep into Java development or just exploring the intersection of frameworks and AI, there's something here for you!…
1 Ep44: Updates in Java tools + MCP and Security 10:58
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Play Later Play Later Lists Like LikedIn this episode, we dive into three key updates from the world of Java development and emerging tech standards: First, walk through a new feature in the Neo4j Java driver (v5.28.5) that enables lightweight object mapping. I’ve set up a sample code repository showcasing how to return Cypher query results directly into your Java domain objects—no full-blown OGM needed. It’s a big improvement, but with a few gotchas you’ll want to understand. Next, we take a look at Jackson Jr , a lightweight version of the popular Jackson library . If you're working in resource-constrained environments or want faster startup times, this stripped-down data processor might be just what your project needs. Finally, we revisit Model Context Protocol (MCP) security, following up on concerns raised in Episode 42. I share two recent articles that highlight current security limitations in MCP and practical tips for developers looking to build safely with it today, even before full support matures. Whether you're optimizing your Java stack or exploring AI protocols, there’s something in this episode for you.…
In this episode, we dive into the latest upgrades in Neo4j tooling, along with recent bug fixes and enhancements in the LLM Knowledge Graph Builder . We also explore a new preview feature for Java object mapping in the Neo4j Java driver and check out the MCP Java SDK . Next, we highlight the new " Using Neo4j with Java " course on GraphAcademy and unpack a compelling Weaviate article on RAG vs. GraphRAG , featuring Microsoft’s GraphRAG methodology. Whether you're a Java dev, graph enthusiast, or AI-curious, there's something in here for you!…
1 Ep42: RAG and Java + AI-generated content and MCP 12:33
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Play Later Play Later Lists Like LikedStar Wars Day is nearly here, and this episode is stacked with tech goodness to celebrate! I’m diving into highlights from the Neo4j ecosystem, starting with an early look at the Using Neo4j with Java course —perfect for getting started with the Java driver in a framework-less setup. Also in this episode: ⚙️ Behind-the-scenes of APOC + Pinecone integration ✨ Part 2 of my Intro to Retrieval Augmented Generation series 🎥 My recent guest spot on Neo4j Live , discussing the Developer’s Guide to Building a Knowledge Graph 🤖 A fascinating series on an AI content experiment from Mark Heckler 📚 Michael Hunger’s must-read blog on the Model Context Protocol (MCP) May the Fourth be with you!…
1 Ep41: Spring AI M7, NODES, RAG + Neo4j, Quarkus, and Intelligent Apps 11:46
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Play Later Play Later Lists Like LikedIn this episode, we unpack a busy week of updates, learning, and cool tech! From Spring AI’s milestone 7 release with simplified Pinecone configuration to some tricky wifi, I walk through recent changes and adventures. Plus, NODES 2025 is officially announced , and there’s hints of our upcoming GraphAcademy Java course. I also talk about the first part of my new blog series on Retrieval Augmented Generation and highlight a fantastic article on Neo4j, Quarkus, and intelligent applications .…
1 Ep40: Recent AI learnings + More than a vector database 10:24
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Play Later Play Later Lists Like LikedFresh from the Arc of AI conference , I’m unpacking the biggest insights that stuck with me—ranging from the extremes of AI’s capabilities to the deeper implications for how we build and maintain our tech systems. I’ll also share a new blog post and code repo I published on loading data into Pinecone, some next-gen tools I’m eyeing, and thoughts on a great article from the Redis blog about why vector databases aren’t enough . Navigate the evolving landscape of LLMs, generative AI, and modern infrastructure with me in this episode.…
1 Ep39: Why embedding models should match + Advice for starting a blog 13:36
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Play Later Play Later Lists Like LikedIn this podcast episode, hear about my hands-on experience ( code repository on Github ) understanding the importance of using the same embedding models for both creating and searching vector embeddings in databases and how mismatched models can lead to poor search results. I also pull highlights from an article with advice for those interested in blogging , and how it particularly relates to my own approach to tech blogging.…
1 Ep38: Spring AI Debugging + How to Contribute to Open Source 13:25
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Play Later Play Later Lists Like LikedIn this episode, I continue my journey with vector databases, integrating Pinecone , Neo4j , and Spring AI . While making some progress, I also encountered hurdles, such as evolving APIs and the unique architecture of vector stores. Next, I share insights from an article on contributing to open-source projects , how it can accelerate your career and enhance both your technical and soft skills. From picking the right project to building credibility within the community, it's a series of steps that gets better with time and practice!…
1 Ep37: Vector Database Frustration + Microsoft LazyGraphRAG 12:22
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Play Later Play Later Lists Like LikedIn this episode, I discuss my challenges exploring vector databases for an upcoming demo. From what is a vector database to integration issues, hear how I tried a few different approaches with limited success and discover the surprising one with the most promise. I also explore Microsoft's "Lazy Graph RAG" approach, which seems to trade one challenge for another but could be valuable in certain cases.…
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1 Ep36: What is a Developer Advocate + Balancing Digital Consumption 11:27
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Play Later Play Later Lists Like LikedThis week, I explored the Javalin Java framework and project decisions I'm trying to make. I also answer: What exactly does a Developer Advocate/Evangelist/Devrel do? Finally, I reflect on an article on balancing digital consumption with actual productivity.
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1 Ep35: Vector Databases + Building Effective Agents 8:53
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Play Later Play Later Lists Like LikedIn this episode, there are two topics I'm looking forward to diving deeper into: vector databases and AI agents. I'm particularly interested in understanding how vector databases work, how they work with data, and their role in AI applications. Then I share my thoughts on Anthropic’s article about Building AI Agents , which discusses their varying definitions—from simple workflows to fully autonomous systems—and provides practical examples. The article highlights the importance of starting simple, adding complexity only as needed.…
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1 Ep34: Outlines and Iterating + Lessons from Grace Hopper 13:21
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Play Later Play Later Lists Like LikedIn this podcast episode, hear my process of preparing for an upcoming conference with insights on how outlines enhance presentations and blog posts, as well as code and architecture. Also discuss how constant improvement is key with an online course as an example. Finally, I highlight a historical read on cryptography , and share reflections from Grace Hopper's 1982 lecture on data, hardware, and software , drawing connections between her insights and modern challenges in technology.…
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1 Ep33: Cypher DSL examples + Crowd-source AI hacking 8:58
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Play Later Play Later Lists Like LikedIn this episode, I share recent blog posts. The first is about tackling challenges with GitHub repositories , offering solutions on syncing forks and using git rebasing, diffs, and pruning. Next, I created examples with Cypher DSL , providing beginner-friendly details to help users get started. Additionally, I started prepping for the Devnexus conference , where I'll be delivering my first keynote. I also read an article about an intriguing AI testing project where a user successfully manipulated an AI to break its rules and win a $50,000 prize, illustrating the potential challenges AI systems face in handling sensitive tasks.…
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In this episode, I’m following up on a few things I’ve mentioned before. I dive into some updates on the Aura CLI (the official product version). I also explored the Neo4j GenAI Java library a bit more, including a connection hiccup I ran into with Neo4j Desktop. Then, I get into my experience with the Cypher DSL library and how Cypher fits into the retrieval part of RAG systems. Finally, I take a fun detour into an interesting article about a GitHub repository that combines coding and sourdough baking . It’s a cool reminder that the skills we learn in coding can apply to all sorts of other areas, even in the kitchen!…
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In this episode, hear updates on a few projects, including Neo4j's new Aura CLI and NeoConverse . Plus, the promised blog post from last week's episode on Spring Data Neo4j entity updates . Shout-out to the JAXJUG and TampaJUG groups. If you're not involved with a tech group, there are options! Finally, a review of the DZone RefCard on Java containerization with its clear guidance on Docker containers, native builds, and deployment, making complex concepts more accessible for developers.…
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1 Ep30: SDN update entity + Knowledge Graph ebook 9:06
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Play Later Play Later Lists Like LikedDive into my latest experiences with Spring Data Neo4j, specifically exploring new methods for updating nodes. After revisiting my 2023 blog post on this topic, I share insights on custom Cypher queries and projections . I also discuss the challenges of finding good examples for update operations. In addition, Neo4j announced the release of The Developer's Guide: How to Build a Knowledge Graph , a new ebook I co-authored. Hear my challenges of writing the book, including finding the right dataset for graph queries, working through product updates during the writing process, and the complexities of collaborating across different perspectives. Stay tuned for future updates, as we continue to refine this ever-evolving resource.…
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1 Ep29: Neo4j GenAI plugin + How-To for Technical Content 10:16
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Play Later Play Later Lists Like LikedIn this episode, dive into a few exciting areas I've been exploring, including generative AI and graph technologies. I worked with Neo4j’s generative AI plugin for an experiment, and it wasn't all smooth sailing. Next, my highlights from an article in the Descript community on how to expand your educational YouTube audience —especially applied for those creating technical content. Key tips include thinking like a viewer to ask great questions, tweaking video intros to keep people engaged, and channeling viewer interests to strengthen connections.…
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1 Ep28: 2024 Recap, 2025 Look-Ahead + Always Be Launching 12:32
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Play Later Play Later Lists Like LikedTake a moment to reflect on 2024, exploring highlights in AI advancements, personal growth, and professional achievements, along with exciting 2025 goals. Then delve into an article that discusses marketing strategies but aligns well with developer advocacy.
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1 Ep27: Lexical vs Semantic Search + Downside of Vector Databases 8:54
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Play Later Play Later Lists Like LikedExplore the distinction between lexical and semantic search technologies, the growing use of vector databases , and the potential challenges and solutions that may arise as these technologies mature.
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1 Ep26: Git Troubles + Dungeons & Dragons Graph 11:43
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Play Later Play Later Lists Like LikedGit issues, finally resetting local branch , cherry-picking , and cleaning up . Then a fun graph presentation - Dungeons, Dragons, and Graph Databases .
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In this episode, hear some positive tech experiences from the week: the upgrade from GitHub Actions ( upload/download artifact ) to v4 through a 3rd party repo , and the new AI coding assistant feature in IntelliJ IDEA.
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1 Ep24: Graph Data Science + Building Knowledge Graphs 9:57
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Play Later Play Later Lists Like LikedThis episode, my refresher with the Graph Data Science library with highlights and challenges. Then an overview of the Building Knowledge Graphs book , a guide on everything from concepts to technical details.
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1 Ep23: Spring Data Neo4j course + Event recap 14:58
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Play Later Play Later Lists Like LikedHear learnings from the new Spring Data Neo4j course - building blocks, updates/deletes, and modeling. Then recap XtremeJ and NODES 2024 , + upcoming at CJUG .
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1 Ep22: LLM Graph Builder + Devrel unlocks “aha” moment 11:12
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Play Later Play Later Lists Like LikedIn this episode, get my latest experiences with the Neo4j LLM Graph Builder , both the benefits and challenges. Then, an article focusing on the concept of the "Aha moment" —a key moment when developers discover the value of a technology.
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In this episode, hear why data (a less glamorous topic) is actually fascinating and crucial. Then, look forward with a preview of next week's NODES 2024 event, which features a range of sessions on graph technologies, AI, data science, and security. Finally, get a teaser for the next episode.
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Cover the basics of graphs , explaining mathematical roots and applications. Introduce Goodhart's Law and highlight the risks of over-focusing on metrics, whether in tech or personal growth.
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Explore agents in AI, distinguishing them from RAG ( article1 , article2 ). And discuss naming and the importance of conveying relevance and clarity.
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1 Ep18: What is Vector search + JConf/Dev2next experiences 10:59
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Play Later Play Later Lists Like LikedWant to take our previous vector topic a step further with vector similarity search . Then, I’ll give my thoughts on 2 recent tech conferences - JConf and dev2next .
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1 Ep17: Using Asciidoc for Tech Content + Current Productivity Reads 10:19
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Play Later Play Later Lists Like LikedThe Asciidoc markup language is powerful and useful for developers and technical writing. Also highlighting productivity books - Atomic Habits and Deep Work - and applications for a developer.
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1 Ep16: Mapping Relationships in Applications + Software Estimates 10:23
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Play Later Play Later Lists Like LikedDocumenting how to map relationship entities from a database to an application. Then, delving into one author’s viewpoint on software project estimates and some ideas to adapt this inaccurate process for better results.
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1 Ep15: What is GraphRAG + Developing Better Software 11:36
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Play Later Play Later Lists Like LikedThe term GraphRAG has recently popped up in the current AI industry, so I review a blog post on what it is and how it fits into the current landscape. I also read an article on ways to improve software development .
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1 Ep14: Making Tech Relatable + Java Entity Mapping 8:47
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Play Later Play Later Lists Like LikedI’m always refreshing my memory and learning new things. How to explain technical concepts to broader audiences, then a question from the community took me into the Java driver docs and entity mapping/ OGMs .
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1 Ep13: GraphQL vs GQL vs Cypher + Road to NODES 9:29
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Play Later Play Later Lists Like LikedHappy Fall! This week, we talk about confusion around GraphQL , GQL , and graph query languages. Then, I am excited to gear up for NODES 2024 by showcasing pre-conference workshops called Road to NODES !
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This week, we discuss retrieval augmented generation (RAG) and how it’s being used to improve responses (or prevent hallucinations) from an LLM. We also chat about an article on how tech conferences have changed and how you can still find value in speaking or attending, plus how to justify the benefits at your organization.…
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1 Ep11: What is a Knowledge Graph + DevUp Conference 9:26
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Play Later Play Later Lists Like LikedA recent blog post defines "knowledge graph", a term that has been around awhile and is part of many recent AI conversations. And then we chat about the DevUp conference I attended last week. Hopefully my thoughts will help you decide how to choose the best event for you.
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1 Ep10: What are embeddings + LLM use case and prompt 7:35
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Play Later Play Later Lists Like LikedWith the amount of new information, sometimes it’s hard to review earlier concepts. In this episode, we explain what an embedding is and talk about aligning your prompt with your use case for improving LLM answers ( example app code repository ).
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1 Ep9: Graphs for Conference Scheduling + Spring AI 1.0 9:57
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Play Later Play Later Lists Like LikedIn preparing for the NODES 2024 conference, I’ll talk about how my colleague and I made scheduling easier by using Neo4j (a database). Spring AI also moved to a 1.0 milestone, and I’ll talk about the easier-than-expected migration (more in my related blog post ).
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1 Ep8: Luck Favors the Prepared + LLM Graph Builder Tool 8:25
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Play Later Play Later Lists Like LikedDo you ever feel like some people have all the luck? Sometimes we need to see things in a new light to break through the barrier or overcome the obstacle. Plus, I took a look at a new-ish tool from Neo4j called the LLM Graph Builder . I’ll give a quick intro on that, plus what I’m doing with it and how it’s going.…
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1 Ep7: Deploying Neo4j on Kubernetes + Parallel Relationship Loading 8:12
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Play Later Play Later Lists Like LikedI now have a code repository and blog post for Neo4j+Kubernetes. We also chat about another blog post on how to solve a deadlock with parallel relationship loading.
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