In the posts in this series, I’ve been taking you through a lot of concepts and tooling. That’s going to continue but, for this post, it felt prudent to take a little break and talk about why doing all this can matter. That gets into interviewing and potentially being hired.
AI and Testing: Scaling Tests
In the previous post, we refactored a test case that we have been working on. In this post, we’re going to use that refactored test case and scale it up a bit.
AI and Testing: Refactoring Tests
In the previous post, we refined an AI test case that we had previously created as a testing example. In this brief post, I want to show a refactoring of that code. We will also align on the output of this test.
AI and Testing: Refining Tests
In the previous post I provided an extended testing example where we wrote an “AI test case” together. This post will provide some more test thinking around that initial test case.
AI and Testing: A Testing Example
In this post, my goal is to write a relatively substantive test case and, while doing so, bring together many of the topics talked about in previous posts of this series.
AI and Testing: LangChain and Orchestration
Here I’m going to continue the thread from the previous post, where we started to look at the concept of Runnables, which is really what puts the “Chain” in “LangChain.”
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AI and Testing: LangChain Messages
In the previous post, we got familiar with LangChain templates and dipped our toes into messages. In this post, I’m going to focus a bit more on those messages since these are the key to communicating with AI.
AI and Testing: LangChain Templates
In this post I’m going to follow the thread from the previous post and dig more into the LangChain ecosystem and start looking at the idea of templates for prompts.
AI and Testing: Local LLMs and LangChain
The previous post covered the concept of Ollama, to get a local LLM on your machine. Here, I’ll focus on using that LLM and introduce two key properties of testability in this context. Doing so will introduce LangChain.
AI and Testing: Ollama and Models
In this post I want to take the initial steps to get some basic tooling available and operating. This is step one if you’re going to work in a technologist context with AI applications.
AI and Testing: Evaluating the Future
As our technocracy continues to grow and as (at least some) technologists continue to push us toward a potentially dehumanized and dehumanizing future, I want to focus on how we can work from within this technocracy to make sure that human experimentation is front and center.
Testing for Quality, Betting on Value
There’s an irony worth noting with my previous posts on Hollywood quality and gaming quality: testing exists, in part, to mitigate risk but only by helping people understand the risks that exist. Yet, quality itself often requires reasoned and reasonable risk-taking! Let’s dig in to this.
The Sunk Cost of Quality: Lessons from Gaming’s Biggest Failures
In the first part of this series, I examined how Hollywood’s financial model (commit hundreds of millions upfront, spend it all before release, then discover whether audiences agree with your projections) creates a high-stakes gamble on predicting quality. Studios bet enormous sums on forecasting how diverse audiences will perceive value years in the future, often with not-so-great results. The gaming industry faces a strikingly similar challenge, but with crucial differences that make the quality prediction problem even more complex.
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The Sunk Cost of Quality: Lessons from Hollywood’s Biggest Failures
When we talk about quality assurance in software, we often treat quality as something measurable, testable, and, if we’re honest, somewhat objective. Does the code work? Does it meet requirements? Does it perform under load? However, quality isn’t entirely objective. It’s a shifting perception of value over time, influenced by customer expectations, cultural context, and changing needs. To understand why this matters, let’s step outside software for a moment and look at an industry that bets hundreds of millions of dollars on predicting quality: Hollywood.
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Guarding Quality From Drift to Discipline
Quality doesn’t collapse overnight. It drifts. It drifts in the seams between teams, in the silence between a feature being built and a feature being tested, in the gap between what we meant to cover and what we actually did. That drift is often invisible. Until it isn’t. That’s why testing can’t live in a corner of the organization. It has to be democratized, distributed, and deliberately practiced. And if we’re serious about doing that, we need ways to see the drift before it becomes damage. Let’s dig in!
Using Narratives to Sharpen Testing Skills
As testers, we spend much of our time reviewing requirements, specifications, and user stories. We’re looking for ambiguities, inconsistencies, and contradictions. However, these analytical muscles can be exercised anywhere, including in narrative fiction. Let’s dig in!
Testing Has Something To Do With Mass Extinction
Okay, I’ll admit my title is a bit of click-bait. The better title would be “Testing Has Something To Do With Paleontology” but even that would not be correct since what I really would have to say is “Testing Has Something To Do With Paleontological Debates About Mass Extinctions in the Fossil Record.” Ugh. Even worse. You know what, let’s just dig in. (Pun slightly intended.)
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The Vibe Around Vibium
There’s a lot of buzz around Jason Huggins’ “Vibium” tool. It’s generating a lot of chatter, but there’s a fair bit of skepticism about whether it’s a tangible product or something more speculative, maybe even a tongue-in-cheek nod to the testing community. Let’s unpack this.
Quality Assurance for Society
As someone who spends their days thinking about quality assurance and testing, I’m trained to look beyond whether something works to ask whether it works well, and for whom. Quality isn’t just about technical functionality; it’s about how humans interact with technology, what happens when systems fail, and whether the design serves user needs or merely designer intentions. These questions become critical when we’re not just testing software, but evaluating proposals to restructure society itself around technological systems.
The Quality Constant: Think and Act Experimentally
I was asked what one piece advice I have that I would give to testers starting out in the industry. It had to just be one piece of advice. It’s an interesting challenge.
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