Generative AI's popularity has led to a renewed interest in quality assurance — perhaps unsurprising given the inherent unpredictability of the technology. This is why, over the last year, the field has seen a number of techniques and approaches emerge, including evals, benchmarking and guardrails. While these terms all refer to different things, grouped together they all aim to improve the reliability and accuracy of generative AI.

To discuss these techniques and the renewed enthusiasm for testing across the industry, host Lilly Ryan is joined by Shayan Mohanty, Head of AI Research at Thoughtworks, and John Singleton, Program Manager for Thoughtworks' AI Lab. They discuss the differences between evals, benchmarking and testing and explore both what they mean for businesses venturing into generative AI and how they can be implemented effectively.

Learn more about evals, benchmarks and testing in this blog post by Shayan and John (written with Parag Mahajani): https://www.thoughtworks.com/insights/blog/generative-ai/LLM-benchmarks,-evals,-and-tests

Software architecture necessarily intersects with a diverse range of critical things, including implementation, infrastructure, data and engineering practices. All these elements require serious consideration and reflection if you're to architect effectively. 

To discuss these various intersections, Thoughtworks' Neal Ford and his long-time collaborator Mark Richards join host Prem Chandrasekaran on the Thoughtworks Technology Podcast. They dive into why these intersections matter, what they mean for software architects and how individuals and teams can go about addressing them. 

Who should be involved in the process of making decisions about software architecture? That's a question that's been puzzling Thoughtworker Andrew Harmel-Law for some time — so much so that he decided to write a book about it. The result is Facilitating Software Architecture. Published by O'Reilly in December 2024, it's both an argument for and a guide to involving more people in the architecture decision process.

To discuss the topic and the book, Andrew joined hosts Neal Ford and Prem Chandrasekaran on the Technology Podcast. They explore why including more roles in software architecture matters today, some of the common objections to and risks of such an approach, alongside techniques and practices that can make doing it in fast-paced and dynamic organizations easier.

"It's quite magical when you see this blossoming of understanding of what it is that architects do... It's not less architecture, it's more. It's just happening in a broader sphere." — Andrew Harmel-Law

You can find Andrew's book on the O'Reilly website: https://www.oreilly.com/library/view/facilitating-software-architecture/9781098151850/

With the rise of generative AI, the concept of the uncanny valley — where human resemblance unsettles, disturbs or disgusts — is more relevant than ever. But is it a problem that technologists need to tackle? Or does it offer an opportunity for greater thoughtfulness about the ways generative AI is being built, deployed and used?

In this episode of the Technology Podcast, host Lilly Ryan is joined by Srinivasan Raguraman to discuss generative AI's uncanny valley and explore how it might offer a model for thinking through our expectations about generative AI outputs and effects. Taking in everything from the experiences of end users to the mental models engineers bring to AI development, listen for a wide-ranging dive into the implications of the uncanny valley in our experience of generative AI today.

Read Srinivasan's recent article (written with Ken Mugrage): https://www.technologyreview.com/2024/10/24/1106110/reckoning-with-generative-ais-uncanny-valley/

Legacy modernization is an enduring challenge — and as systems become more complex, the difficulty of understanding and modelling a system so it can be modernized only becomes more difficult. However, at Thoughtworks we've seen some recent success bringing generative AI into the legacy modernization process.

To discuss what this means in practice and the benefits it can deliver, host Ken Mugrage is joined by Thoughtworks colleagues Shodhan Sheth and Tom Coggrave. Shodhan and Tom have been working together in this space in recent months and, in this episode of the Technology Podcast, offer their insights into finding success with this novel combination. They explain how it can be implemented, the challenges and experiments they did on their way to positive results and what it means for how teams and organizations think about modernization in the future.

Read Shodhan and Tom's article on legacy modernization and generative AI (written with Alessio Ferri):  https://martinfowler.com/articles/legacy-modernization-gen-ai.html

Data contracts are a bit like APIs for data — they make it possible to interface with data in a way that ensures the transfer of data from one place to another is stable and reliable. This is particularly important for building more reliable data-driven applications.

To discuss data contracts, host Lilly Ryan is joined on the Technology Podcast by Andrew Jones, the creator of the data contract concept (in 2021) and author of Driving Data Quality with Data Contracts (2023), and Thoughtworker Ryan Collingwood who is currently writing their own book on data contracts due to be published in 2025. Andrew and Ryan offer their perspectives on the topic, explaining the origins and motivation for the idea and outlining how they can be used in practice. 

You can find Andrew’s book here: https://www.amazon.com/Driving-Data-Quality-Contracts-comprehensive/dp/1837635005

What does it mean to be a technology leader today? What kind of challenges must you address? What questions do you need to answer? To explore all that — and dive into what it looks like from a Thoughtworks perspective — host Ken Mugrage spoke to Thomas Squeo, the CTO for Thoughtworks in the Americas.

They discuss everything from keeping track of emerging technologies and wider industry shifts, to product thinking, AI and career development. Listen to get to know a Thoughtworks leader and discover fresh perspectives on some of the big questions and debates all of us in tech keep finding ourselves returning to. 

Find Thomas on X: @squeot

LinkedIn: https://www.linkedin.com/in/thomassqueo/

Volume 31 of the Technology Radar will be released on October 23, 2024. As always, it will feature 100+ technologies and techniques that we've been using with clients around the world. Alongside them will be a set of key themes that emerged during the process of putting it together. We think they offer another way into the Radar and give a unique insight on some of the most interesting issues impacting the software industry.

In this episode of the Technology Podcast we discuss them: coding assistance antipatterns, Rust being anything but rusty, the rise of WebAssembly and what we describe as the "cambrian explosion of generative AI tools." To do so, Alexey Boas is joined by guests and podcast regulars Ken Mugrage and Neal Ford. Ken and Neal provide an insight into the conversations that happened during the process, and offer their perspective on the implications of these themes for the wider tech industry.

The Thoughtworks Technology Radar is, first and foremost, a publication. It's a document that anyone in the tech industry can read twice a year to learn about our experiences and perspectives on technology. However, it's also more than that: it's built on top of a process of deliberation, discussion and curation. We think that's particularly important — it's something we encourage technology teams and organizations to do and which we support with our Build Your Own Radar tool. 

On this episode of the Technology Podcast, Neal Ford and Ken Mugrage join Prem Chandrasekaran to discuss Build Your Own Radar. They outline why the Radar process is just as important as the artifact that gets created at the end, and explain how organizations can use it to facilitate conversations about how and what technology they use and want to use in the future. 

Learn more about Build Your Own Radar: https://www.thoughtworks.com/radar/byor

There are no shortage of options when it comes to relational databases. While the likes of PostgreSQL have proven enduring, even as the market has evolved, for data scientists and data engineers that need to manage and query particularly complex or large data sets, the most popular databases aren't always right for the job. Thankfully, this is where projects like DuckDB can help. Built for what's called 'vectorized query execution', it's well-suited to the demands of online analytical processing (OLAP).

To get a deeper understanding of DuckDB and how the product has developed, on this episode of the Technology Podcast, hosts Ken Mugrage and Lilly Ryan are joined by Thoughtworker Ned Letcher and Thoughtworks alumnus Simon Aubury. Ned and Simon explain the thinking behind DuckDB, the design decisions made by the project and how its being used by data practitioners in the wild.

Learn more about DuckDB: https://duckdb.org/why_duckdb.html

Explore Ned and Simon's book Getting Started with DuckDB: https://www.amazon.com/Getting-Started-DuckDB-practical-efficiently/dp/1803241004