Prompt Engineering Conference

Goda Go is a TEDx speaker, AI educator, and YouTube content creator with a passion for making artificial intelligence accessible, practical, and creative. With a background in architecture and a dynamic career spanning marketing, project management, and entrepreneurship, she brings a unique perspective to the intersection of technology and creativity. As the founder of AI Productivity Hub and co-host of the Prompt Engineering Conference, Goda empowers individuals and businesses to harness the power of generative AI tools. Her mission: to inspire everyone to become a creator in the digital age.

Maxim Salnikov is a seasoned technology leader with 20+ years in software development and 6+ years driving go-to-market strategy in cloud and AI. At Microsoft, he leads the Digital & App Innovation business across 10 Western European countries, accelerating adoption of Azure and GitHub among startups and enterprises alike. A passionate advocate for developer success, Maxim blends strategic leadership with deep technical expertise in cloud-native, AI, and modern app development.

Outside of his corporate role, Maxim is a global tech community builder and keynote speaker. He has founded leading conferences such as Prompt Engineering Conf, ngVikings, Mobile Era, and PWA Summit, and delivered 200+ talks in 26 countries. His mission: to empower developers to create the next generation of intelligent applications.

Dan Cleary is a serial tech founder and the Co-founder & CEO of PromptHub, a platform helping individuals and teams write, iterate, and collaborate on AI prompts. With a strong background in product development and a track record of founding successful startups like Tether and Modus, Dan blends technical insight with a deep understanding of user needs. He's passionate about making prompt engineering accessible, insightful, and even fun—sharing weekly updates and practical tips through his popular AI newsletter.

From prompt to play: witness how cutting-edge generative models, clever chaining and a sprinkle of game-dev know-how can birth an end-to-end, fully-playable beat ’em up... Including art, music, code and lore.... just in my spare time. Get inspired, steal workflows, ship your dream game.

Jesus has 30 years of experience in the digital sector, including 19 years at Microsoft, where he specialized in blending Artificial Intelligence and User Experience, drawing on his background as both an engineer and designer. In 2014, he became the inventor of a protected Microsoft patent, earning recognition for his innovative work.Jesus has collaborated with over 200 clients across 35 countries in industries like sports, entertainment, media, and retail. He has led the design and development of consumer apps with millions of downloads and is an award-winning speaker with over 100 sessions and keynotes at global events. He also serves as an AI Tutor and Course Co-Director at the University of Oxford.

AI Coding Assistants promise to revolutionise developer productivity, yet many engineers struggle to achieve consistent results. Drawing from my twelve month journey with these tools, this talk reveals practical strategies for transforming frustrating experiences into reliable outcomes. I'll share specific techniques that have consistently produced good quality code, concrete examples of successful prompting patterns, and resources that developers can use. Whether you're new to AI assistants or looking to refine your approach, you'll leave with a better understanding of the potential of these tools' to improve your development efficiency.

I am a developer advocate for open source, currently working at AWS. I am passionate about, and help customers be successful in using open source.

Ever found yourself doing the "vibe coding" dance? You know, where you have a brilliant idea but end up spending countless hours refactoring because the implementation didn't quite match your vision? What if I told you there's a better way to vibe with AI?

Enter "vibe prompting" – the art of having meaningful conversations with AI before diving into code. Instead of immediately jumping into implementation and dealing with multiple refactoring cycles, we'll explore how to craft intentional dialogues that shape better outcomes from the start. Some strategic planning will allow you to use fewer tokens than refactoring your code a hundred times trying to hone in on what you really needed to build in the first place.

Ready to take your vibe coding to the next level? Let's explore how "vibe prompting" can transform your development workflow.

I am a long-time open-source developer, educator, manager and mentor. I encourage everyone around me to share their collective knowledge, and am a strong champion of diversity in the tech industry. When I'm not working, I'm helping others with career coaching and interview preparation, tinkering with 3D printing or IoT projects, or telling dad jokes. I'm originally from Canada but currently live in the USA.

Getting an AI-generated image to capture nuanced, context-specific details—such as authentic cultural references—can be surprisingly difficult. In this session, we’ll explore a practical, reasoning-driven workflow for bridging that gap. The process begins by constructing a detailed persona for a subject-matter expert (for example, a historian of Japanese culture). Using a reasoning engine, we then generate prompts from the expert’s perspective, feed them into an image generation model, and finally, apply AI-based analysis to evaluate the result—closing the loop with metacognitive feedback. I’ll share real-world examples where this approach produced strikingly authentic and context-aware images, along with tips for adapting it to your own creative and professional use cases.

Ajit Jaokar is a Lectu Engineering Sciences at the University of Oxford, where he also serves as Course Director for the "Artificial Intelligence: Cloud and Edge Implementations" program. As an AI Ambassador at Oxford, he leads interdisciplinary AI reskilling initiatives and is actively shaping the future of AI education. Ajit is a Senior AI Fellow at the UK Ministry of Justice’s Justice AI Unit, advising on the national rollout of large-scale AI systems. He is the founder of Erdos Research and feynlabs, and his work focuses on early-stage AI prototypes and democratizing AI through accessible education. Ajit is a frequent speaker at global forums including the World Economic Forum, the European Parliament, and the G7. A passionate advocate for neurodiversity, he is currently writing a book on teaching AI through mathematical foundations to high school students.

Over the last 18 months, I didn't just write a book about the AI revolution; I wrote a book with it. My book, From Cloud Native to AI Native, Catching the Next Wave of Innovation, was created in a deep, evolving partnership with generative AI. This talk is the story of our collaboration. I will take you from my initial, tentative prompts to the complex dialogues that shaped entire chapters, sharing real examples of prompts and their raw outputs. We'll explore how my own skills had to evolve alongside the AI's capabilities. This journey provides a powerful parallel to the core message of my book: becoming "AI native" isn't about replacing human skill but augmenting it. You will leave with a clear-eyed view of how to move beyond simple queries and integrate LLMs into any serious, professional workflow.

Building AI agents is no longer the hard part—making them reliable is. This talk dives into the under-discussed but critical practice of context engineering: the techniques used to control what your agents “see,” “remember,” and respond to.

The session will examine how to design effective context flows across planning, execution, and memory stages using frameworks such as LangGraph and Google’s Agent Development Kit (ADK). Through real-world examples and a demo, we’ll explore how to handle context drift, token overflow, and prompt injection. I’ll also share a before-and-after demo of a flaky agent that becomes robust simply by improving its context strategy.

This session will equip attendees (whether they’re building single-agent copilots or complex multi-agent systems) with practical tools to debug, refine, and productionize agent behavior through clean, modular context pipelines.

I'm a Tech Lead at Red Buffer, where I design and deploy generative AI systems for real-world use—ranging from healthcare automation to multi-agent copilots for SaaS platforms. I'm also a Google Developer Expert (GDE) in AI, with a focus on making advanced LLM systems accessible, robust, and production-ready.

When I'm not leading AI projects or running workshops, I mentor learners at Turing College as a Senior Team Lead, helping them bridge the gap between theory and real-world engineering. I also write for Towards Data Science, where I break down complex AI topics with a focus on practical use cases.

Outside of tech, I have a deep love for classic literature (yes, Tolstoy counts—even if he's Russian), strong coffee, and the quiet company of cats. I'm driven by curiosity, clarity, and the joy of building things that make life a little smarter and simpler.

Prompt engineering is rapidly emerging as a critical skill for anyone working with Large Language Models. This session will demystify the process, blending the art of intuitive communication with the science of structured querying. We will cover core concepts such as prompt structure, context injection, and task specification, alongside practical strategies for eliciting creative text generation, accurate information retrieval, and effective problem-solving from LLMs. Learn how to master the nuances of prompting and unlock the full potential of these powerful AI tools.

Dr. Roushanak Rahmat is a Google Developer Expert and AI Strategy Leader with a PhD in Artificial Intelligence, specialising in computer vision and image processing. She has designed and delivered AI and Generative AI solutions for leading organisations including IBM, NHS, Elekta, Coca-Cola, Lloyds, and Scottish Power — turning complex challenges into impactful business outcomes.

Currently serving as Lead Data Scientist at IBM Consulting, Dr. Rahmat drives AI and GenAI strategies for clients in healthcare, finance, and energy. Previously at Elekta, she spearheaded AI innovation projects to enhance radiotherapy treatment through advanced deep learning models and multimodal imaging data. Her technical expertise spans MRI, CT, CBCT, and large-scale data analysis using Python, PyTorch, Keras, TensorFlow, and Jax.

Beyond her technical leadership, she is a passionate advocate for AI ethics, diversity, and inclusive innovation. As a Women Techmakers Ambassador and an active public speaker, she shares insights on AI development, adoption, and strategy through Medium articles, YouTube videos, and open-source contributions.

Dr. Rahmat excels at bridging the gap between technical teams and strategic stakeholders, fostering collaboration, and making AI accessible to all. Portfolio: roushanakrahmat.github.io

As LLMs evolve into multi-step agents, evaluation must move beyond input-output correctness. This session shows how to assess both node-level decisions and full agentic flows, with a live demo of multi-node agents and practical eval techniques.

Hilik Paz is the Co-Founder and CTO of arato.ai, a platform dedicated to enabling teams to build, evaluate, and deploy reliable GenAI agents. With a strong foundation in software engineering and a keen focus on applied AI, Hilik has been instrumental in developing tools that bridge the gap between LLM capabilities and real-world applications.

Discover how Meta Agents transform natural-language instructions into fully operational Al agents. In this session, we'll explore the "vibe-driven" approach to architecting Al Agents - where you describe the Al Agent you need, and a Meta Agent designs, builds, and deploys it for you. The Meta Agent captures your requirements, organizes the right tools and knowledge bases, defines the system prompt, and handles AWS deployment. Participants will see a live demonstration in which we will interact with a Meta Agent to create a new Al agent, and we will test it together. We will unpack the design decisions, methodology, and process, leaving the audience with practical skills to architect and deploy their own version of Meta Agents.

Carlo Peluso is a Senior Solutions Architect at Storm Reply, focusing on Generative AI solutions and cloud-native data architectures. Since 2022, he has been supporting organizations in designing and scaling AI-driven and data platforms on the cloud. He is also an AWS Community Builder and a frequent speaker at conferences and tech events on Generative AI.

Guido Nebiolo is a seasoned IT professional with over 10 years of experience and a strong background in cloud architectures, software engineering, and artificial intelligence. Having held various leadership and technical roles, he is currently a BU Manager at Storm AI Reply, a leading cloud and AI consulting company. In this role, he oversees strategic AI initiatives and leads a specialized team delivering Generative AI solutions. Guido helps organizations scale their AI capabilities through both technical implementation and organizational change management, serving clients across multiple industries. He is also an AWS Community Builder and AWS User Group Leader for AWS Turin (Italy) UG, and an active speaker at multiple conferences and events.

In case you didn't know, the era of the prompt as the sole monarch of AI engineering is over. For years, we've treated prompts like magic incantations, but this narrow focus creates brittle, amnesiac systems that fail in the real world. The simple prompt is dead. Its successor? Context Engineering.

This session is a practical guide to this new paradigm, showing you how to build truly intelligent, production-ready AI agents using Google's Agent Development Kit. We will move beyond the single prompt and learn to architect the entire information ecosystem—the rich, dynamic context—that allows Gemini to perform complex, multi-step tasks with stunning accuracy.

You will leave with a new playbook for building sophisticated AI, including:

• A Live Demo in Firebase Studio: We will build and deploy a context-aware sales assistant, demonstrating how to orchestrate Google's powerful Agentic Workspace.
• Mastering Retrieval with Vertex AI Search: Learn to ground your agents in reality by dynamically fetching information from your own knowledge bases, eliminating hallucinations.
• Interacting with the World via Function Calling: See how to give your agent "hands" by enabling it to interact with external APIs and data sources in real-time.
• Deploying for Scale with Vertex AI Agent Engine: Understand the path from a prototype to a scalable, production-grade agent.

It's time to stop whispering into a keyhole and start architecting the world behind the door. The prompt had its reign. Now, let us build the future with context and Google Cloud.

Muhammad Ahsan Ayaz is a Google Developers Expert in Angular, an author of 3 books, and a Software Architect at Scania. He has received multiple awards as an educator, created open-sourced projects used by thousands of developers, and created engaging video courses as tutorials which hundreds of developers benefit from, every day.

Are you ready to harness the full power of AI, but not sure where to start? This presentation is for you! We'll demystify prompt engineering and show you how to build a custom prompt library and internal training program right within your own agency or team.

Many agencies struggle to consistently leverage AI across all their services. We faced the same challenge and developed a sustainable framework to empower our various teams and roles, including sales, business development, marketing, strategy, campaign management, development and project management. In this session, we'll share our journey, including the pitfalls we encountered, so you can avoid common mistakes.

You'll learn practical strategies for:

• Crafting effective prompts that get the results you need, with consideration for the job to be done and client needs
• Organizing your prompts into a valuable, easy-to-use library, including how to iteratively improve on prompts as a team
• Training your team to confidently and consistently use AI tools, including selecting the right model and tool for the job to be done

Join us to discover how a well-designed prompting framework can streamline your workflows, enhance client support, and unlock new efficiencies for agencies.

With over 18 years of experience, Richard embodies a key value of RDEL Group: “Explore What’s Possible”. As a Business and Commerce Marketing graduate from the John Molson School of Business at Concordia, Richard is responsible for RDEL's products and operations, including AI. He ensures the goals and objectives of our partners and clients are met by leading RDEL’s AI and paid media teams. Richard's role extends into the oversight of all reporting, technology, and ancillary marketing services that RDEL offers.

During Richard’s previous roles with RDEL, he shepherded major changes to the marketing division which resulted in significant growth for the company. At the paid media division, Media Propulsion Laboratory, what was once a three person team has expanded to an 12 person team within the last 2 years with an impressive client roster. The seamless expansion would not have been possible without Richards' keen leadership skills.

Richard has secured impressive results for MPL’s clients. For example through-out our nearly 10 year partnership with Xplore, a rural and remote internet service provider, he secured an overall conversion increase of 221%, accompanied by a 43% reduced cost per acquisition (CPA).

Prior to joining RDEL, Richard managed and grew accounts at The Canadian Professional Sales Association, AutoTrader, Xplore (formerly Xplornet), and VIA Rail Canada.

This talk presents a shift in prompt engineering from intuition and guesswork to formal, quantitative evaluation using reproducible metrics. Attendees will explore practical tools, experimental frameworks, and platforms like PromptLayer that enable rigorous benchmarking, monitoring, and improvement of prompt performance. Designed for engineers, researchers, and product leaders, the session equips participants with the knowledge to build scalable, reliable, and scientifically grounded prompt systems.

Abhiram is a Senior Data Scientist at Publicis Sapient, where he drives innovation in natural language processing as part of the firm’s specialist Data Science team. He holds a Master’s in Data Science from King’s College London and brings deep, cross-functional expertise across AI, quantum computing, brain-computer interfaces, and large-scale product development.

He has worked on cutting-edge data science initiatives at Ai Palette, a Singapore-based startup applying AI to CPG innovation, and Collinson, a UK-based global leader in customer loyalty and travel experiences, where he focused on personalization and text mining at scale. An experienced Mozilla Tech Speaker, Abhiram has delivered talks at premier global conferences including PyCon, MozFest, and CodeMash. His LinkedIn Learning course on Rust programming has been taken by over 80,000 learners worldwide.

Abhiram has published research at IEEE and ACM conferences and previously spent over four years at SAP Labs Bengaluru as a developer and research fellow, specializing in web development, computer vision, and robotic process automation (RPA).

With hands-on experience in developing and deploying NLP products—including applications of BERTopic for topic modeling—Abhiram offers practical insights into how large language models are reshaping topic discovery. His recent talk at Analytics Vidhya’s DataHour series, attended by over 4,200 participants, received a 4.6/5 feedback score, underscoring his ability to communicate complex ideas with clarity and impact.

In this talk we'll walk the audience through the modern AI-powered service set up, from the very basics to technicalities of hosting AI applications in a cloud.

We will describe how to convert the idea into the AI-powered pipeline and then what challenges awaits you when rendering this theoretical concept into live. We will talk about: * Structured inputs and outputs and how engineer the context * How to host such an application in a cloud and what building blocks will you need * The importance of observing and monitoring the performance: what problems to expect and how to battle them * The criticality of avoiding vendor lock-in and the need to experiment quickly with models and prompts

Sergey Konstantinov is an experienced web developer with over 15 years in the field, from heading a 50M DAU API service to working as a tech lead in a startup. He is particularly interested in APIs as a broad topic, about which he has given many speeches and authored two books.

Alena Panshina is a product manager and consultant with 13 years in tech. Former Yandex PM and ex-CEO of a $2M AI startup, she has been working with LLMs since 2019. She has scaled products from zero to millions of users and now helps companies build AI-powered solutions. Based in Ireland, she has traveled to 40 countries.

The majority of Model Context Protocol (MCP) use right now is directly through end user applications like Claude Desktop or Cursor. However, pretty much any Large language Model, from small open models, to the APIs of the frontier models can be prompted to call tools and retrieve external resources, including those exposed using the Model Context Protocol (MCP).

Doing this effectively in your projects is key to the development of agentic software.

This talk will explore how different models respond to different types of tool use prompting, along with the benefits and pitfalls prompting to generate tool calls in different data formats. We will go through some real examples of interacting with MCP servers, and look at the prompts used by some popular agentic frameworks and tools.

Experienced software professional focussed on Machine Learning and Apps with a passion for Open Source AI and a strong interest in novel ways Machine Learning can be used on locally on low power devices.

In 2024 Richard delivered a talk about using the open source CodeGemma model for Google DevFest Scotland, and spoke about the current state of Open Source AI at the Macc Tech event.

Richard is the CTO at Blue Beck Ltd, and has a background in backend service and mobile development, going back to when the first releases of the Android and iOS SDKs became available, on other mobile devices before that, having started his career at RARE as a console game developer.

In the world of AI-powered SaaS, the true competitive edge lies not just in building smart products but in building smart teams. As prompt engineering becomes a critical layer between human intention and machine output, founders must rethink how they attract, assess, and retain top talent. In this talk, we’ll explore how prompt engineering is redefining roles, streamlining hiring processes, and enabling leaner, more productive teams. Drawing on real lessons from scaling an AI-first company, we’ll share actionable strategies for leveraging AI and prompt engineering to enhance team performance, navigate emerging talent trends, and scale effectively without compromising your company’s human core.

Ernesto Spruyt is an entrepreneur, CEO, and global tech talent pioneer behind Tunga, connecting Africa’s tech experts with innovative companies worldwide. With a passion for psychology and culture, Ernesto bridges Europe’s boardrooms and Africa’s emerging tech scenes, breaking down barriers along the way. Known for his unconventional approach and infectious enthusiasm, he inspires audiences with humor, sharp insights, and a refreshing call to rethink work, leadership, and purpose.

AI is like steroids. It can speed up the process, but you still have to go to the gym and practice the fundamentals. You have to suffer. (c) In this talk, I will explore the art and science of writing actionable, high-context prompts that transform AI from a "hallucinating assistant" into a productive teammate, without skipping the basics.

We will start with a brief historical overview, comparing today’s Prompt-Driven Development (PDD) to the Readme-Driven Development (RDD) practices of the past. I will present practical techniques for maximizing the quality of AI-generated code by carefully tuning context, specificity, and scope, all demonstrated with concrete and relatable examples.

I will also describe the concept of AI wrappers, the essential middleware behind tools like Cursor and Windsurf. Wrappers automatically gather context from the codebase, tailor prompts for optimal results, refine AI output, and embed changes directly inside editors like VS Code. This section will clarify why prompt engineering is a critical modern skill.

I will also show my personal prompt collection in action. Then we will break down what makes an effective prompt and what does not, comparing and iterating live. I will illustrate why different scenarios require different types of prompts and explain why this matters for developers at all levels, not just juniors.

To wrap up, I will outline key principles for AI-assisted development, including requirements analysis, collaborative habits, validation practices, and robust documentation. Attendees will leave with actionable guidance for integrating prompt-driven practices into their daily workflow.

Slides What is PDD The Evolution: From RDD to PDD Prompting Matters: Context, Specificity, Scope My Prompt Collection Why We Need Different Prompts Why Prompting Is Important (not only for juniors) The Golden Rules of Vibe Coding

Platform Engineer | OSS Contributor

Vlad is a seasoned full-stack developer with over a decade of experience building and maintaining scalable B2B platforms, as well as a dedicated open-source contributor. He currently works at Cybergizer in a hybrid SWE/CRE/SRE role, focusing on production reliability, systems design, and cross-functional engineering using languages like Ruby, Elixir, and Rust.

He is a member of the Diesel.rs contributor team and the creator of opencryptolist.xyz, a platform dedicated to fostering open-source contributions in the blockchain industry. Vlad is also the author of getIDL.xyz – a tool to get IDL and source code info from closed-source Solana programs, using AI-based reverse engineering. He has open-sourced, maintained, and contributed to several libraries in the Ruby and Rust ecosystems (including pkcs12cracker, solscan-mcp, and visual-cryptography).

He is also a writer for EffectiveProgrammer, AI Advances, IT Next, and Level Up Coding, and a three-time hackathon winner.

Large Language Models (LLMs) often fail not because they lack capability, but because the prompts guiding them are inefficient. Manual prompt engineering is slow, inconsistent, and nearly impossible to scale.

In this talk, I’ll share how I tackled this in my open-source project, prompt-eng-gsm8k-gpt3.5-dspy, where I used DSPy to automate prompt creation and tuning for the GSM8K mathematical reasoning benchmark.

By defining tasks declaratively in DSPy and letting its compiler handle generation and refinement, I compared zero-shot (55.0%), few-shot (60.5%), chain-of-thought (68.0%), self-consistency (72.5%), Prolog-style (71.0%), and my Enhanced Prolog method — which achieved 74.5% accuracy, a 19.5% gain over zero-shot and 6.5% over CoT, while using less time and tokens than self-consistency.

This Enhanced Prolog approach structures the LLM’s reasoning as logical facts and inference rules, making outputs: • More accurate by avoiding reasoning leaps • Debuggable via traceable steps • Machine-verifiable through logical consistency checks

Attendees will learn: 1. How DSPy automates and scales prompt optimization. 2. When to use zero-shot, CoT, few-shot, self-consistency, or logic-based prompting. 3. How Prolog-style reasoning improves reliability and explainability. 4. Setting up A/B testing pipelines for prompt evaluation. 5. Debugging workflows for more trustworthy LLM outputs.

Packed with real metrics, open-source code, and a reproducible workflow, this talk moves you from guesswork to data-driven prompt engineering that’s explainable, efficient, and production-ready.

I am a Master’s student in Artificial Intelligence at Royal Holloway, University of London, specializing in prompt engineering, LLM optimization, and NLP. I created the open-source project prompt-eng-gsm8k-gpt3.5-dspy, where I used DSPy to automate prompt creation for the GSM8K benchmark and developed my Enhanced Prolog-style reasoning method, boosting GPT-3.5 accuracy from 55.0% (zero-shot) to 74.5%.

I have hands-on experience testing prompt strategies — zero-shot, few-shot, chain-of-thought, self-consistency, Prolog-style, and DSPy-optimized — across multiple models, including GPT-3.5, LLaMA-2-7B-Chat-HF, and Qwen. My work combines reproducible code, measurable results, and practical workflows that bridge research and real-world applications.

Outside AI, I enjoy doodling, playing badminton, and mentoring peers in AI projects. And while I don’t have a definitive favorite member of One Direction, I’ll say Harry Styles for his creativity — a trait I also bring to my AI experiments.

Discover how Generative AI is revolutionizing security audits — boosting speed, accuracy, and adaptability in today’s complex compliance ecosystems. This session brings to life real-world use cases where GenAI doesn’t just optimize workflows — it redefines the entire audit approach. We’ll unpack how strategic and precise prompt engineering increase the efficiency of audits, streamline policy adherence, and empower decision-makers with more intelligent oversight. From initial planning to tailored observations and reporting, witness how GenAI turns audits into a proactive security asset — not just a checkbox exercise. Whether you're battling audit fatigue or building future-ready audit process, this talk is your blueprint for smarter, faster, and more resilient security audits and governance.

I'm a Senior IT Internal Auditor at American Express Global Business Travel, specializing in GRC strategy, internal audits, and cybersecurity frameworks like ISO 27001, SOX and PCI DSS. I'm leading a multi-year initiative to integrate Generative AI into audit processes, including prompt engineering techniques that boost precision and adaptability. My blog on the Autopsy forensic tool has over 75,000 views and ranks #1 on Google search results — proof of my commitment to making complex tech both accessible and impactful.

Even the most skilled developers can hit a wall—stress and burnout silently sabotage focus, creativity, and productivity. What if the same AI tools you use to write code could also coach your mind to stay calm, clear, and resilient?

In this talk, I’ll show how to design prompts that go beyond outputs—transforming Large Language Models into adaptive AI “coaches” for wellbeing. Drawing on clinical hypnotherapy, life coaching, business psychology, and my experience as a Java developer who experienced burnout firsthand, I’ll share how prompts can: Reduce cognitive overload and decision fatigue Reframe stressful challenges into actionable steps Create AI-guided daily reset rituals Maintain sustainable focus without sacrificing creativity.

You’ll walk away with ready-to-use prompt templates that transform AI into a proactive wellbeing partner—helping you not just survive, but thrive in high-pressure tech roles.

Gena Frangina is a Clinical Hypnotherapist, Software Engineer (Java), IT Wellbeing Strategist, and Mindvalley HoloBody Coach. After experiencing a panic attack while coding during the pandemic, she dedicated herself to mastering hypnotherapy and coaching strategies for preventing burnout.

Today, she blends hypnotherapy, coaching, and software engineering expertise to help tech professionals use AI not just for productivity, but as personal wellbeing partners—empowering them to build resilience, focus, and sustainable performance in high-pressure environments.

AI offers a wide array of applications, from audio transcription and image description to writing assistance, proofreading, content translation, and diverse general prompting purposes.

For implementing these kinds of use cases, you broadly have three options:

• Cloud solutions – send your data to an AI model running on a powerful server in the cloud
• On-device solutions – download a (possibly large) model file to the client and run it locally
• Shared models – use models built into a device or browser (the focus of this talk)

On the Chrome team, we’re proposing a number of APIs that we and partners we’ve worked with think would be worthwhile to have as common Web platform APIs.

In this talk, I’ll:
- Give an overview of these APIs
- Briefly demo some implementations in Chrome
- Open the floor for general discussion and feedback

Thierno currently works as a Web Ecosystem Engineer @Google focusing on on integrating Gemini into Google Chrome and making Web AI accessible for everyone.

The Model Context Protocol (MCP) is a great base protocol that allows AI agents to connect to 3rd party systems, and has gained rapid adoption across the industry. However, it requires designing APIs with specific patterns that make them easy for AI agents to use.

Your organisation likely already has pre-existing APIs with machine readable schemas, like GraphQL or OpenAPI (REST). While they expose useful business data and capabilities, these schemas are not optimised for consumption by AI agents. Still, they have mature frameworks around them and importantly – are already implemented.

In this talk we'll share what we learned at Isometric when building a translation layer between GraphQL and MCP. We optimised for avoiding duplicate work and making the most of existing tooling built into the GraphQL server, while at the same time ensuring that the end result follows all the MCP server design best practices and can be effectively used by AI agents.

Our internal GraphQL server has more than 800 types, 100 top-level query fields and 200 mutations. We'll show how we leveraged GraphQL schema introspection, query templates and complexity heuristics to distill this large API surface into a small, but scalable, set of MCP tools.

You’ll learn about practical strategies for exposing your existing APIs to AI agents without rewriting the entire API layer. We'll discuss specific challenges like handling GraphQL's infinite nesting problem and demonstrate how complex GraphQL queries get transformed into simpler, AI-friendly MCP tools.

Konrad Komorowski is a software engineer at Isometric, working on carbon removal credit verification. He's spent his career between big tech and startups – building probabilistic ads measurement systems as a tech lead at Meta, and helping scale engineering organisations at companies like Glovo from 150 to 500 engineers. He's passionate about finding simple & robust solutions to complex, real-world problems, believing the best engineering happens where theory meets practice.

A simple Pull Rrequest to translate the Prompt Engineering Guide sparked a journey—from a late-night side project to mentoring in OSSCA’s LLM program. This talk shares how curiosity, community, and open source shaped a growing AI movement in Korea.