Simon Maple
The Power of Prompting with AI Native Development - Simon Maple
#1about 7 minutes
Demonstrating the limits of iterative AI prompting
A live coding demo shows how iterative, stateless prompts fail to build robust features, highlighting the need for a persistent specification.
#2about 5 minutes
Understanding when prompting fails and how LLMs process requests
LLMs struggle with overly large contexts, sub-token character manipulation, and math, and they process prompts sequentially, which impacts how they interpret constraints.
#3about 8 minutes
Advanced techniques for writing effective and structured prompts
Improve LLM outputs by using structured formatting like XML tags, framing tasks with constraints upfront, and using a 'say' operation to guide the model's thought process.
#4about 6 minutes
Why code-centric development becomes fragile and hard to maintain
In traditional development, code becomes the source of truth, coupling the 'what' with the 'how' and making the system brittle as requirements, bugs, and framework changes accumulate.
#5about 4 minutes
Moving to a spec-centric model with AI
A spec-centric approach makes the specification—combining requirements and tests—the long-lived source of truth from which disposable code is generated.
#6about 9 minutes
Key benefits of the AI-native development paradigm
AI-native development creates adaptable software through context, enables autonomous maintenance for security and performance, and makes development more inclusive for non-coders.
#7about 4 minutes
Exploring the future and community of AI-native tools
An overview of the Tessl platform, the AI Native Dev community, and a landscape of tools that are shaping the future of specification-driven development.
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