Prompt

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About Prompt

Recent discussions on AI prompting highlight the effectiveness of using "gates"—specific conditions that must be satisfied before proceeding—over traditional rules. This approach emphasizes the importance of verifiable steps, such as holding URLs, and enhances the integration of external elements, or "hooks," into the prompting process. The trend suggests a shift towards more structured and accountable methods in AI interactions.

Why AI prompting works better with “gates” than rules
Prompt

Why AI prompting works better with “gates” than rules

A recent post by the author breaks down “gates” in AI prompts—explicit conditions that must be met before an agent can move on. Unlike rules that can be hand-waved, gates force checkable steps (like holding URLs) and pair well with external “hooks.”

Miniature de la vidéo: RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models
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RAGPrompt

RAG vs Fine-Tuning vs Prompt Engineering: Optimizing AI Models

When an LLM’s answer changes depending on training data or knowledge cutoffs, “better prompts” aren’t the only option. This video breaks down three practical ways to improve chatbot output—what each one adds, and what it costs.

  • Clarify the differences between RAG, fine-tuning, and prompt engineering as distinct methods for getting better model responses.
  • Understand RAG step-by-step (retrieval → augmentation → generation), including how embeddings enable semantic matching across internal documents.
  • See what fine-tuning actually changes (model weights via supervised input–output pairs), and why it can be faster at inference time but harder to maintain.
  • Learn where prompt engineering helps (format, context, examples) and where it can’t (teaching truly new information).
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