fewshot prompting

If you can share the article text or link, I’ll tailor this precisely. Provisional synopsis based on the title: :q!Explains advanced prompt patterns for building reliable LLM workflows: modular task decomposition, role- and few-shot scaffolding, retrieval-augmented context, and self-critique/evaluation loops with tool use. It shows how to chain prompts, add guardrails, and iterate with tests and metrics to raise accuracy, consistency, and maintainability across creative, analytical, and agentic tasks.

Could you share the blog article text or a link to “Prompt Optimisation”? I’ll craft a 250–500 character synopsis based on its content.

Chain-of-thought prompting elicits step-by-step reasoning from language models. The article explains how revealing intermediate steps can boost accuracy on complex math, logic, and planning tasks, surveys prompting tactics (few-shot exemplars, rationale cues, self-consistency), weighs trade-offs (verbosity, latency, leakage), and offers tips for evaluation, trimming, and safe deployment.

Guidance on crafting effective prompts: clarify the goal and audience; include essential context and constraints; specify desired format and tone. Use examples and stepwise instructions, keep language precise, and iterate with A/B tests. Tune parameters, add safety and evaluation checks, and version/document prompts to boost reliability and consistency.

I don’t have the article text. Please paste the “Learning Prompt Engineering” blog (or a link), and I’ll craft a 250–500 character synopsis. If you can’t share it, provide key points (thesis, 3–5 takeaways, audience, tone), and I’ll summarize. Alternatively, I can write a generic synopsis of common prompt-engineering topics—just confirm if that works.