Posts Tagged ‘DevoxxUK2025’
[DevoxxUK2025] Mastering Prompt Engineering for Immersive Text-Based Adventures
At DevoxxUK2025, Charles-Philippe Bernard, a software engineer at JPMorgan in Glasgow, captivated attendees with his talk on mastering prompt engineering through his remastered 1980s text-based adventure game, SRAM. Using the Godot engine, a WebSocket Python server, and Ollama for local LLM inference with Llama 3.1, Charles showcased how carefully crafted prompts bring dynamic interactions to life. His presentation explored the art of prompt engineering, demonstrating how to shape AI responses for immersive gameplay, manage game states, and handle NPC interactions. Through practical examples, he shared techniques to harness AI’s potential while navigating its quirks, such as hallucinations, offering developers actionable insights to create engaging experiences.
Crafting the System Prompt
Charles began by emphasizing the importance of a well-defined system prompt, which sets the tone and context for the LLM. In SRAM, the prompt establishes the AI as the “Game Master,” named Gun Master, responsible for narrating the adventure in a JSON-formatted output. This structure includes speaker ID, response text, and actions, ensuring consistency across interactions. By injecting variables like scene state and inventory, Charles demonstrated how the prompt adapts dynamically, enabling the game to track items like a knife or navigate scenes. He stressed the need for clear, structured instructions to guide the LLM, especially for smaller models like Llama 3.1’s 7-billion-parameter version, which may struggle with complex tasks.
Managing Game State and NPCs
A key challenge in SRAM is maintaining the game’s state, including inventory, scene descriptions, and NPC interactions. Charles explained how the prompt template incorporates variables to reflect the player’s progress, such as adding a knife to the inventory after picking it up. For NPCs, like the leprechaun Fergus, he crafted specific instructions to define personality, tone (e.g., a humorous Irish accent), and behavior, using few-shot examples to steer responses. However, he noted challenges like the LLM repeating examples verbatim or hallucinating actions, which he mitigates by balancing creativity (via a temperature of 0.8) with structured outputs to ensure consistency.
Handling AI Quirks and Hallucinations
Charles candidly addressed the LLM’s limitations, particularly hallucinations, where the model generates unexpected or incorrect actions, like responding to “make me a pizza” outside the game’s context. By setting a temperature of 0.8, he balances creativity with adherence to instructions, though this sometimes leads to inconsistent outputs. He shared techniques like explicit instructions (e.g., listing no items in the inventory) and iterative prompt refinement, often using larger models like ChatGPT to improve prompts for smaller, local models. Charles also highlighted the importance of testing prompts with humans to ensure clarity, as unclear instructions confuse both humans and AI.
Practical Tips for Prompt Engineering
To master prompt engineering, Charles recommended starting with a clear, structured prompt template, using markdown or bullet points for readability. He advised including specific guidelines, like short responses or JSON formatting, and leveraging few-shot examples to guide the model. For smaller models, verbose yet clear instructions are crucial, as they lack the reasoning power of larger frontier models. Charles also emphasized iterative refinement, storing interactions for testing consistency, and using tools like uppercase keywords or structured formatting to enhance the model’s understanding. His approach empowers developers to create dynamic, AI-driven experiences while managing the inherent challenges of LLMs.
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[DevoxxUK2025] Platform Engineering: Shaping the Future of Software Delivery
Paula Kennedy, co-founder and COO of Cintaso, delivered a compelling lightning talk at DevoxxUK2025, tracing the evolution of platform engineering and its impact on software delivery. Drawing from over a decade of experience, Paula explored how platforms have shifted from siloed operations to force multipliers for developer productivity. Referencing the journey from DevOps to PaaS to Kubernetes, she highlighted current trends like inner sourcing and offered practical strategies for assessing platform maturity. Her narrative, infused with lessons from the past and present, underscored the importance of a user-centered approach to avoid the pitfalls of hype and ensure platforms drive innovation.
The Evolution of Platforms
Paula began by framing platforms as foundations that elevate development, drawing on Gregor Hohpe’s analogy of a Volkswagen chassis enabling diverse car models. She recounted her career, starting in 2002 at Acturus, a SaaS provider with rigid silos between developers and operations. The DevOps movement, sparked in 2009, sought to bridge these divides, but its “you build it, you run it” mantra often overwhelmed teams. The rise of Platform-as-a-Service (PaaS), exemplified by Cloud Foundry, simplified infrastructure management, allowing developers to focus on code. However, Paula noted, the complexity of Kubernetes led organizations to build custom internal platforms, sometimes losing sight of the original value proposition.
Current Trends and Challenges
Today, platform engineering is at a crossroads, with Gartner predicting that by 2026, 80% of large organizations will have dedicated teams. Paula highlighted principles like self-service APIs, internal developer portals (e.g., Backstage), and golden paths that guide developers to best practices. She emphasized treating platforms as products, applying product management practices to align with user needs. However, the 2024 DORA report reveals challenges: while platforms boost organizational performance, they often fail to improve software reliability or delivery throughput. Paula attributed this to automation complacency and “platform complacency,” where trust in internal platforms leads to reduced scrutiny, urging teams to prioritize observability and guardrails.