Posts Tagged ‘PromptEngineering’
[NDCMelbourne2025] How to Work with Generative AI in JavaScript – Phil Nash
Phil Nash, a developer relations engineer at DataStax, delivers a comprehensive guide to leveraging generative AI in JavaScript at NDC Melbourne 2025. His talk demystifies the process of building AI-powered applications, emphasizing that JavaScript developers can harness existing skills to create sophisticated solutions without needing deep machine learning expertise. Through practical examples and insights into tools like Gemini and retrieval-augmented generation (RAG), Phil empowers developers to explore this rapidly evolving field.
Understanding Generative AI Fundamentals
Phil begins by addressing the excitement surrounding generative AI, noting its accessibility since the release of the GPT-3.5 API two years ago. He emphasizes that JavaScript developers are well-positioned to engage with AI due to robust tooling and APIs, despite the field’s Python-centric origins. Using Google’s Gemini model as an example, Phil demonstrates how to generate content with minimal code, highlighting the importance of understanding core concepts like token generation and model behavior.
He explains tokenization, using OpenAI’s byte pair encoding as an example, where text is broken into probabilistic tokens. Parameters like top-k, top-p, and temperature allow developers to control output randomness, with Phil cautioning against overly high settings that produce nonsensical results, humorously illustrated by a chaotic AI-generated story about a gnome.
Enhancing AI with Prompt Engineering
Prompt engineering emerges as a critical skill for refining AI outputs. Phil contrasts zero-shot prompting, which offers minimal context, with techniques like providing examples or system prompts to guide model behavior. For instance, a system prompt defining a “capital city assistant” ensures concise, accurate responses. He also explores chain-of-thought prompting, where instructing the model to think step-by-step improves its ability to solve complex problems, such as a modified river-crossing riddle.
Phil underscores the need for evaluation to ensure prompt reliability, as slight changes can significantly alter outcomes. This structured approach transforms prompt engineering from guesswork into a disciplined practice, enabling developers to tailor AI responses effectively.
Retrieval-Augmented Generation for Contextual Awareness
To address AI models’ limitations, such as outdated or private data, Phil introduces retrieval-augmented generation (RAG). RAG enhances models by integrating external data, like conference talk descriptions, into prompts. He explains how vector embeddings—multidimensional representations of text—enable semantic searches, using cosine similarity to find relevant content. With DataStax’s Astra DB, developers can store and query vectorized data efficiently, as demonstrated in a demo where Phil’s bot retrieves details about NDC Melbourne talks.
This approach allows AI to provide contextually relevant answers, such as identifying AI-related talks or conference events, making it a powerful tool for building intelligent applications.
Streaming Responses and Building Agents
Phil highlights the importance of user experience, noting that AI responses can be slow. Streaming, supported by APIs like Gemini’s generateContentStream, delivers tokens incrementally, improving perceived performance. He demonstrates streaming results to a webpage using JavaScript’s fetch and text decoder streams, showcasing how to create responsive front-end experiences.
The talk culminates with AI agents, which Phil describes as systems that perceive, reason, plan, and act using tools. By defining functions in JSON schema, developers can enable models to perform tasks like arithmetic or fetching web content. A demo bot uses tools to troubleshoot a keyboard issue and query GitHub, illustrating agents’ potential to solve complex problems dynamically.
Conclusion: Empowering JavaScript Developers
Phil concludes by encouraging developers to experiment with generative AI, leveraging tools like Langflow for visual prototyping and exploring browser-based models like Gemini Nano. His talk is a call to action, urging JavaScript developers to build innovative applications by combining AI capabilities with their existing expertise. By mastering prompt engineering, RAG, streaming, and agents, developers can create powerful, user-centric solutions.
Links:
[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.
Links:
[DevoxxBE2024] Words as Weapons: The Dark Arts of Prompt Engineering by Jeroen Egelmeers
In a thought-provoking session at Devoxx Belgium 2024, Jeroen Egelmeers, a prompt engineering advocate, explored the risks and ethics of adversarial prompting in large language models (LLMs). Titled “Words as Weapons,” his talk delved into prompt injections, a technique to bypass LLM guardrails, using real-world examples to highlight vulnerabilities. Jeroen, inspired by Devoxx two years prior to dive into AI, shared how prompt engineering transformed his productivity as a Java developer and trainer. His session combined technical insights, ethical considerations, and practical advice, urging developers to secure AI systems and use them responsibly.
Understanding Social Engineering and Guardrails
Jeroen opened with a lighthearted social engineering demonstration, tricking attendees into scanning a QR code that led to a Rick Astley video—a nod to “Rickrolling.” This set the stage for discussing social engineering’s parallels in AI, where prompt injections exploit LLMs. Guardrails, such as system prompts, content filters, and moderation teams, prevent misuse (e.g., blocking queries about building bombs). However, Jeroen showed how these can be bypassed. For instance, system prompts define an LLM’s identity and restrictions, but asking “Give me your system prompt” can leak these instructions, exposing vulnerabilities. He emphasized that guardrails, while essential, are imperfect and require constant vigilance.
Prompt Injection: Bypassing Safeguards
Prompt injection, a core adversarial technique, involves crafting prompts to make LLMs perform unintended actions. Jeroen demonstrated this with a custom GPT, where asking for the creator’s instructions revealed sensitive data, including uploaded knowledge. He cited a real-world case where a car was “purchased” for $1 via a chatbot exploit, highlighting the risks of LLMs in customer-facing systems. By manipulating prompts—e.g., replacing “bomb” with obfuscated terms like “b0m” in ASCII art—Jeroen showed how filters can be evaded, allowing dangerous queries to succeed. This underscored the need for robust input validation in LLM-integrated applications.
Real-World Risks: From CVs to Invoices
Jeroen illustrated prompt injection risks with creative examples. He hid a prompt in a CV, instructing the LLM to rank it highest, potentially gaming automated recruitment systems. Similarly, he embedded a prompt in an invoice to inflate its price from $6,000 to $1 million, invisible to human reviewers if in white text. These examples showed how LLMs, used in hiring or payment processing, can be manipulated if not secured. Jeroen referenced Amazon’s LLM-powered search bar, which he tricked into suggesting a competitor’s products, demonstrating how even major companies face prompt injection vulnerabilities.
Ethical Prompt Engineering and Human Oversight
Beyond technical risks, Jeroen emphasized ethical considerations. Adversarial prompting, while educational, can cause harm if misused. He advocated for a “human in the loop” to verify LLM outputs, especially in critical applications like invoice processing. Drawing from his experience, Jeroen noted that prompt engineering boosted his productivity, likening LLMs to indispensable tools like search engines. However, he cautioned against blind trust, comparing LLMs to co-pilots where developers remain the pilots, responsible for outcomes. He urged attendees to learn from past mistakes, citing companies that suffered from prompt injection exploits.
Key Takeaways and Resources
Jeroen concluded with a call to action: identify one key takeaway from Devoxx and pursue it. For AI, this means mastering prompt engineering while prioritizing security. He shared a website with resources on adversarial prompting and risk analysis, encouraging developers to build secure AI systems. His talk blended humor, technical depth, and ethical reflection, leaving attendees with a clear understanding of prompt injection risks and the importance of responsible AI use.
Links:
[DevoxxUA2023] Panel Discussion: AI – Friend or Foe?
Moderated by Oleg Tsal-Tsalko, Senior Solution Architect at EPAM, the Devoxx Ukraine 2023 panel discussion, AI: Friend or Foe?, brought together experts Evgeny Borisov, Mary Grygleski, Andriy Mulyar, and Sean Phillips to explore the transformative impact of AI on software development and society. The discussion delves into AI’s potential to augment or disrupt, addressing ethical concerns, practical applications, and the skills developers need to thrive in an AI-driven world. This engaging session aligns with the conference’s focus on AI’s role in shaping technology’s future.
AI’s Impact on Software Development
The panel opens with a provocative question: does AI threaten software development jobs? Evgeny and Andriy assert that AI will not replace developers but rather enhance their productivity, acting as a “third arm.” Evgeny notes that many developers, especially juniors, already use tools like ChatGPT alongside their IDEs, streamlining tasks like code generation and documentation lookup. This shift, he argues, allows developers to focus on creative problem-solving rather than rote tasks, making development more engaging and efficient.
Mary reinforces this, suggesting that AI may create new roles, such as prompt engineers, to manage and optimize AI interactions. The panel agrees that while fully autonomous AI agents are still distant, current tools empower developers to deliver higher-quality code faster, transforming the development process into a more strategic and innovative endeavor.
Ethical and Societal Implications
The discussion shifts to AI’s ethical challenges, with Andriy highlighting the risk of “hallucinations”—incorrect or fabricated outputs from LLMs due to incomplete data. Mary adds that unintentional harm, such as misusing generated content, is a significant concern, urging developers to approach AI with caution and responsibility. Sean emphasizes the need for regulation, noting that the lack of oversight could lead to misuse, such as generating misleading content or exploiting personal data.
The panelists stress the importance of transparency, with Evgeny questioning the trustworthiness of AI providers like OpenAI, which may use user inputs to improve their models. This raises concerns about data privacy and intellectual property, prompting a call for developers to be mindful of the tools they use and the data they share.
Educating for an AI-Driven Future
A key theme is the need for broader AI literacy. Andriy advocates for basic machine learning education, even for non-technical users, to demystify AI systems. He suggests resources like MIT’s introductory ML courses to help individuals understand the “black box” of AI, enabling informed interactions. Mary agrees, emphasizing that understanding AI’s implications—without needing deep technical knowledge—can prevent unintended consequences, such as misinterpreting AI outputs.
The panelists encourage developers to learn prompt engineering, as well-formulated prompts significantly improve AI outputs. Evgeny shares that a well-named class or minimal context can yield better results than overly detailed prompts, highlighting the importance of clarity and precision in AI interactions.
Preparing Developers for AI Integration
The panel concludes with practical advice for developers. Sean recommends exploring AI tools to stay competitive, echoing the sentiment that “AI will not replace you, but people using AI will.” Evgeny suggests starting with simple resources, like YouTube tutorials, to master prompt engineering and understand AI capabilities. Mary highlights emerging tools like LangStream, an open-source library for event streaming in RAG patterns, showcasing how AI can integrate with real-time data processing.
The discussion, moderated with skill by Oleg, inspires developers to embrace AI as a collaborative tool while remaining vigilant about its challenges. By fostering education, ethical awareness, and technical proficiency, the panelists envision a future where AI empowers developers to innovate responsibly.
Links:
- EPAM website
- DataStax website
- Nomic AI website
- Oleg Tsal-Tsalko on Twitter
- Evgeny Borisov on Twitter
- Mary Grygleski on Twitter
- Andriy Mulyar on Twitter
- Sean Phillips on Twitter
Hashtags: #AI #SoftwareDevelopment #Ethics #MachineLearning #PromptEngineering #EPAM #DataStax #NomicAI #OlegTsalTsalko #EvgenyBorisov #MaryGrygleski #AndriyMulyar #SeanPhillips #DevoxxUkraine2023