Press "Enter" to skip to content

Category: Generative AI

Choosing a Chunking Strategy for RAG Applications

Published 2026-10-22 by Kevin Feasel

Bala Priya C puts together a decision tree:

In this article, you will learn how to choose the right chunking strategy for a retrieval-augmented generation system by working through a structured decision tree tied to the properties of your documents and retrieval task.

Topics we will cover include:

  • What each major chunking strategy assumes about your documents, and when those assumptions hold.
  • A six-question decision tree that maps concrete document and retrieval conditions to specific chunking strategies.
  • The failure signals that indicate a chosen strategy is not working, and the fixes that tend to resolve them.

Click through for the list of strategies and when to include each.

Leave a Comment

Lessons Learned from Troubleshooting with LLMs

Published 2026-10-21 by Kevin Feasel

Grant Fritchey spends a weekend:

Grant Fritchey walks through a real troubleshooting saga – using Wine, a shim DLL, a misconfigured registry entry, and a lot of trial and error – to get a BridgeCom Maverick radio’s Windows CPS running on Linux Mint.

Along the way, repeated dead ends revealed a pattern: AI tools stay locked onto whatever direction you last pointed them, and it’s up to you to redirect, request alternatives, and demand measurement over guesswork.

In this article, Grant shares everything he learned from the experience, including 8 key takeaways to help you avoid similar trouble when using AI in the future.

Grant takes us through the story and shares eight tips for better troubleshooting along the way. Oh, and Wine is notoriously finicky, so I can totally get why it’d take a while to sort out the issues Grant had.

Leave a Comment

Connecting Azure Databricks to a Microsoft Foundry AI Gateway

Published 2026-10-20 by Kevin Feasel

Drew Furgiuele connects the dots:

While some people might look at the different capabilities and think this is an “either/or” scenario, the reality is that most Azure customers are going use (or even need to use) both: developers building apps and agents in Databricks, for example, may want to leverage a model or other agent deployed in Foundry. Meanwhile, developers in Foundry may need to call a Databricks managed MCP service.

These types of calls aren’t all that difficult; in fact, both platforms provide built-in connectors or UI solutions to make this happen: simply provide the service endpoint, pick your authentication method, and you’re pretty much done. However, the introduction of the respective gateways, especially on the Foundry side, can make this just a little more complicated. Let’s look at one example.

Click through for that issue and how Drew was able to work around it.

Leave a Comment

Adding Temporal Reasoning to RAG

Published 2026-10-02 by Kevin Feasel

Ivan Palomares Carrascosa checks the date:

Topics we will cover include:

  • How to extend standard subject-predicate-object triples into time-stamped quadruples stored in a simple temporal graph.
  • How to calculate recency weights with exponential decay and use them to rank conflicting facts as of a given query date.
  • How to tune the half-life parameter and integrate the temporal graph into a deterministic 3-tiered Graph-RAG retrieval pipeline.

This is especially important if you’re searching over the news or other systems that value more recent information over older information.

Comments closed

Using the Azure Resiliency Agent

Published 2026-09-24 by Kevin Feasel

Reitse Eskens gives a copilot a spin:

And that isn’t limited to services like storage, web portals, user management, and data stuff. It’s also location, backups, and disaster recovery. But there’s a catch: you need to make sure this is configured. I always compare the cloud (Azure in my daily work) with a huge box of Lego. You have all the pieces and elements to make something cool, but you have to build it. Even when you automate it, you still need to think about what you want.

Now, before I continue my Azure Fundamentals training, let’s move on to what this post is about: resiliency. Or, how your environment is set up for disaster recovery.

Let’s use the Azure Copilot to guide the process, create the necessary resource and review the advice.

Click through to see how it works. Because it’s in preview right now, we don’t know how much it will cost later. But in the meantime, you can give it a try.

Comments closed

Building Daily Averages in DAX

Published 2026-09-24 by Kevin Feasel

Marco Russo and Alberto Ferrari take generative AI tooling for a spin:

Computing an average daily sales amount is straightforward: you should iterate over the dates, compute sales for each day, and average the results. An AI assistant can generate DAX code that works in just a few seconds; whether it computes the number you need is a different question.

In this article, we ask AI to generate every measure, function, and validation query. We start with a simple average and test it against more demanding scenarios. Our job is to explain the business requirements, challenge the proposed code, and verify the results. Each test reveals a missing detail from the original request.

The prompts below show a conversation with an AI agent. They describe how to get the DAX code from the AI. They are not a verbatim transcript: a different agent might produce different expressions.

This shows the duality of generative AI outcomes: you can end up with a good solution, but unless you know the ins and outs of what you’re doing, you can end up in a sub-optimal situation.

Comments closed

Managing AI Context

Published 2026-09-09 by Kevin Feasel

Eugene Meidinger has a pair of articles. The first one covers the use of Markdown for building AI context:

When performing agentic development, most of your time will be spent either writing prompts or context files in Markdown (.md) files. This is a simple format used for documentation and notetaking, but which is also easy to read or write for humans and agents. You have almost certainly encountered Markdown before, but you may not have known it. Markdown is used by AI agents, in code documentation, and in some chat programs. Even Microsoft Teams supports a subset of Markdown.

Markdown is extremely common in more than just AI development work, so it’s definitely worth knowing.

The other is a guide to understanding and managing AI agent context:

The most important task for you during agentic development is to create, gather, and curate context. Context is anytext (or images) that the model ingests to produce a more relevant and useful response. Providing the right context at the right time helps agents produce better results; it’s about more than just writing markdown files:

Comments closed

Excel Semantic Table Add-In

Published 2026-08-31 by Kevin Feasel

Teo Lachev has written a plugin:

It dawned on me that while waiting on Microsoft, I could fill these gaps myself by creating an Excel add-in. That is how the Semantic Table add-in was born. It picks up right where default connected tables leave off.

Others have already explored this path, such as Jörg Schmidt with his PBIXL add-in. However, I’ve taken a different implementation approach focused on these core features:

Click through for the approach, as well as an important warning that this is a very early beta. I think the GitHub repo is this one, as Teo didn’t include the link in his post as of the time my post went live.

Comments closed

Building a Mental Model of LLMs

Published 2026-08-25 by Kevin Feasel

John Mount puts together an idea:

As of now (late 2026) LLM (large language model) technology providers, users, and work products flood the public commons. It therefore makes sense to have even a primitive mechanistic mental model of these technologies. You are forced to have an opinion. Without a mechanism or model one tends to fall into disempowering anthropomorphic language. Some clear thoughts on this can be found here and here.

In this note I would like to try and outline a (very) simplified mental model of LLM mechanics and mechanisms. By “mental model” I mean a cartoon to work through in your mind, not a model of the LLMs as having their own mind. I won’t be teaching the history of LLMs, how to build them, how to use them, or their moral or philosophic implications. I will only try to give a very rough outline how the current (2026) LLMs work.

Click through for an intuitive explanation of how they work.

Comments closed

Visual Design and Generative AI

Published 2026-08-21 by Kevin Feasel

Cole Nussbaumer Knaflic guides the non-thinking machine:

Now that we have the story planned, it’s time to start developing the content that will support our message and narrative. When data is part of that, a good first step is choosing a visual that aids in comprehension. The right graph makes your point immediately clear. The wrong one makes your audience spend their mental energy decoding the graph instead of understanding your message.

This is where people sometimes stumble. They use the first chart that comes to mind—or simply carry forward the one they used during exploratory analysis. But a graph that works for exploring data isn’t necessarily the best for communicating it. Your audience and takeaway should drive the choice. By this point, you’ve already done that work: you know your audience, you’ve planned your story, and you’ve written takeaway titles that tell you exactly what each graph needs to show. Let those sentences guide your design.

As you’d expect, there’s some good advice on choosing specific types of visuals. But the majority of this article is around nudging the language model to spit out the correct visual for the right reasons.

Comments closed