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When we need an LLM to perform a complex task, we often need to feed it a lot of context. Coming up with a design for a new feature requires descriptions of how we want the feature to appear to the user, guidelines on how it should be implemented, information on external systems to consult, and so on. All this can be several pages of markdown. The obvious way to do this is for a human to write thi
In the early 1960s, Fred Brooks managed the development of IBM's System/360 computer systems. After it was done he penned his thoughts in the book The Mythical Man-Month which became one of the most influential books on software development after its publication in 1975. Reading it in 2026, we'll find some of it outdated, but it also retains many lessons that are still relevant today. The book con
How to make LLM-assisted changes governable, reviewable, and reusable LLM programming assistants have demonstrated considerable value, but mostly with individual developers. The internal IT organization in Thoughtworks has been using them for their teams and have developed a method and workflow called Structured Prompt-Driven Development (SPDD). The article describes a simple example of this workf
As we see LLMs churn out scads of code, folks have increasingly turned to Cognitive Debt as a metaphor for capturing how a team can lose understanding of what a system does. Margaret-Anne Storey thinks a good way of thinking about these problems is to consider three layers of system health: Technical debt lives in code. It accumulates when implementation decisions compromise future changeability.
Every AI interaction generates signal: prompts that worked, context that was missing, patterns that succeeded, failures worth preventing. Most teams discard this signal. I propose a structured feedback practice that harvests learnings from AI sessions and feeds them back into the team's shared artifacts, turning individual experience into collective improvement. 08 April 2026 Rahul is a Principal
To let coding agents work with less supervision, we need ways to increase our confidence in their result. As software engineers, we have a natural trust barrier with AI-generated code - LLMs are non-deterministic, they don't know our context, and they don't really understand the code, they think in tokens. This article explores a mental model that brings together emerging concepts from context and
AI coding assistants respond to whoever is prompting, and the quality of what they produce depends on how well the prompter articulates team standards. I propose treating the instructions that govern AI interactions (generation, refactoring, security, review) as infrastructure: versioned, reviewed, and shared artifacts that encode tacit team knowledge into executable instructions, making quality c
An Architecture Decision Record (ADR) is a short document that captures and explains a single decision relevant to a product or ecosystem. Documents should be short, just a couple of pages, and contain the decision, the context for making it, and significant ramifications. They should not be modified if the decision is changed, but linked to a superseding decision. As with most written documents,
I’ll start with some more tidbits from the Thoughtworks Future of Software Development Retreat ❄ ❄ We were tired after the event, but our marketing folks forced Rachel Laycock and I to do a quick video. We’re often asked if this event was about creating some kind of new manifesto for AI-enabled development, akin to the Agile Manifesto (which is now 25 years old). In short, our answe
Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development. It was very interesting to read OpenAI’s recent write-up on “Har
I’ve been busy traveling this week, visiting some clients in the Bay Area and attending The Pragmatic Summit. So I’ve not had as much time as I’d hoped to share more thoughts from the Thoughtworks Future of Software Development Retreat. I’m still working through my notes and posting fragments - here are some more: ❄ ❄ What role do senior developers play as LLMs become established? A
Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development. The number of options we have to configure and enrich a coding a
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development.
Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development. The discourse about to what level AI-generated code should be re
I’m about to head away from looking after this site for a few weeks (part vacation, part work stuff). As I contemplate some weeks away from the daily routine, I feel an urge to share some scattered thoughts about the state of LLMs and AI. ❄ ❄ ❄ ❄ I’ve seen a few early surveys on the effect AI is having on software development, is it really speeding folk
From Strategy to Real Results OKRs have become a popular way to connect strategy with execution in large organizations. But when they are set in a top‑down cascade, they often lose their meaning. Teams receive objectives they didn’t help create, and the result is weak commitment and little real change. High‑performing teams work in another way. They define their own objectives in an organization t
It's become a common habit for developers to give Large Language Models (LLMs) a persona when working with them. The first time I ran into this was my colleague Birgitta characterizing her LLM as a stubborn donkey (called “Dusty”). She picked it because it had the traits of: eager to help stubborn very well-read, but inexperienced (for Dungeons and Dragons fans: high intelligence, low wisdom) won’
Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development. Ever since AI coding assistance started gaining traction I’ve he
Like most loudmouths in this field, I've been paying a lot of attention to the role that generative AI systems may play in software development. I think the appearance of LLMs will change software development to a similar degree as the change from assembler to the first high-level programming languages. The further development of languages and frameworks increased our abstraction level and product
As computer systems get more sophisticated we've seen a growing trend to value deep specialists. But we've found that our most effective colleagues have a skill in spanning many specialties. We are thus starting to explicitly recognize this as a first-class skill of “Expert Generalist”. We can identify the key characteristics of people with this skill - and thus recruit and promote based on it. We
Building AI Agents that interact with the external world. While LLMs excel at generating cogent text based on their training data, they may also need to interact with external systems. Function calling allows them to construct such calls. The LLM does not execute these calls directly, instead it creates a data structure that describes the call, passing that to a separate program for execution and
Birgitta is a Distinguished Engineer and AI-assisted delivery expert at Thoughtworks. She has over 20 years of experience as a software developer, architect and technical leader. This article is part of “Exploring Gen AI”. A series capturing Thoughtworks technologists' explorations of using gen ai technology for software development. As agentic coding assistants become more capable, reactions vary
Shodhan Sheth is head of Enterprise Modernization and Principal Technologist at Thoughtworks. He is always looking to apply technology towards positive business outcomes Since the release of ChatGPT in November 2022, the GenAI landscape has undergone rapid cycles of experimentation, improvement, and adoption across a wide range of use cases. Applied to the software engineering industry, GenAI assi
As we move software products using generative AI technology from proof-of-concepts into production systems, we are uncovering a range of common patterns. Evals play a central role in ensuring that these non-deterministic systems are operating within sensible boundaries. Large Language Models need enhancement to provide information beyond a generic and static training set. Most of the time we can d
Design tokens are design decisions as data and serve as a single source of truth for design and engineering. Utilizing deployment pipelines, they enable automated code generation across platforms, allowing for faster updates and improved consistency in design. Organizing tokens in layers—progressing from available options to tokens that capture how they are applied—ensures scalability and a better
When a single-page application needs to fetch data from a remote source, it needs to do so while remaining responsive and providing feedback to the user during an often slow query. Five patterns help with this. Asynchronous State Handler wraps these queries with meta-queries for the state of the query. Parallel Data Fetching minimizes wait time. Fallback Markup specifies fallback displays in marku
Measuring developer productivity is a difficult challenge. Conventional metrics focused on development cycle time and throughput are limited, and there aren't obvious answers for where else to turn. Qualitative metrics offer a powerful way to measure and understand developer productivity using data derived from developers themselves. Organizations should prioritize measuring developer productivity
Improvements in communications technology have led an increasing number of teams that work in a Remote-First style, a trend that was boosted by the forced isolation of Covid-19 pandemic. But a team that operates remotely still benefits from face-to-face gatherings, and should do them every few months. Remote-first teams have everyone in a separate location, communicating entirely by email, chat, v
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