#prompt engineering techniques

Learning Lab

Zero-Shot vs Few-Shot vs Chain-of-Thought: Pick the Right Technique

Zero-shot, few-shot, and chain-of-thought are three distinct prompting techniques with different accuracy, latency, and cost profiles. Learn when to use each, how to combine them, and how to measure which approach works best for your specific task.

Apr 15, 2026 · 15 min read

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Learning Lab

Write Like a Human: AI Content Without the Robot Voice

AI-generated content defaults to averaging—safe, professional, and indistinguishable. Learn four techniques to inject real voice into your outputs: specificity constraints, pattern matching from your own writing, temperature tuning, and the constraint-audit pass that removes robotic patterns.

Apr 14, 2026 · 5 min read

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Learning Lab

When to Fine-tune, Prompt, or Use RAG — A Decision Framework

Fine-tuning, prompt engineering, and RAG solve different problems at different costs. This framework shows you which one actually fits your constraints — with real data, decision matrices, and production-tested workflows.

Apr 14, 2026 · 13 min read

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Learning Lab

Zero-Shot vs Few-Shot vs Chain-of-Thought: When Each Works

Zero-shot, few-shot, and chain-of-thought solve different prompting problems. Learn when each technique works, when it fails, and how to layer them in production systems.

Apr 13, 2026 · 5 min read

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Learning Lab

Fine-Tuning vs Prompt Engineering vs RAG: Pick the Right Tool

Fine-tuning, prompt engineering, and RAG solve different problems. Learn which approach fits your use case, when each one fails, and a practical decision tree for choosing the right tool.

Apr 3, 2026 · 5 min read

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Learning Lab

Zero-Shot vs Few-Shot vs Chain-of-Thought: Which Prompting Technique Actually Works

Three core prompting techniques, each with a different accuracy-speed trade-off. Learn when to use each one, what they cost in tokens, and how to decide which works for your task.

Mar 30, 2026 · 5 min read

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Learning Lab

Why LLMs Hallucinate and 4 Ways to Stop It

LLMs hallucinate because they predict tokens, not facts. Learn exactly why this happens and four production-tested techniques to reduce errors—from grounding prompts in real data to verification loops that catch false citations.

Mar 28, 2026 · 5 min read

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Learning Lab

Structured Prompting Gets 3x Better Outputs. Here’s the Framework

Structured prompting—defining exact output formats, constraints, and validation rules in your prompt—increases extraction accuracy from ~67% to 94%. This pillar covers the four-layer framework (schema, constraints, template, validation), working examples from production, model comparisons, and the implementation stack you need.

Mar 28, 2026 · 11 min read

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Learning Lab

Fine-Tuning vs Prompt Engineering vs RAG: Which Actually Works

Three paths to better LLM performance: prompt engineering, RAG, and fine-tuning. Learn exactly when to use each, why teams pick wrong, and the cost-benefit math that determines which actually makes sense for your use case.

Mar 26, 2026 · 6 min read

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