📅 April 14, 2026⏱ 8 min read✍️ MoltBot Engineering
Prompt EngineeringLLM Techniques

Advanced Prompt Engineering: Chain-of-Thought, Few-Shot & System Prompt Design

Basic prompting gets you 60% of the way. These five advanced techniques reliably close the gap — improving accuracy, consistency, and output quality on the tasks that matter most in production.

Prompt engineering has a split reputation. Critics say it's fragile voodoo. Practitioners who've moved beyond "tell the model what you want" to principled prompt architecture know that the right techniques reliably add 10–30% accuracy on complex tasks.

Five techniques that move the needle

1. Chain-of-Thought (CoT)

Ask the model to show its reasoning before giving a final answer. "Think step by step" or "before answering, reason through the problem." Dramatically improves accuracy on multi-step reasoning, math, and logical deduction tasks.

+15–30% accuracy on reasoning tasks

2. Few-Shot Prompting (with selection)

Include 3–5 worked examples in the prompt. The key insight most teams miss: example selection matters enormously. Pick examples that are semantically similar to the input query using embedding similarity, not random or fixed examples.

+10–20% accuracy vs. zero-shot

3. Self-Consistency

Generate 5–10 outputs for the same prompt with temperature > 0, then take the majority vote (for classification) or best-of-n (for generation). Reduces variance and improves accuracy without any additional training.

+5–15% accuracy; 3–5× more reliable

4. System Prompt Architecture

Structure system prompts as: Role → Context → Constraints → Output format → Examples. Each section does specific work. Role sets the model's perspective. Constraints prevent unwanted outputs. Output format prevents parsing failures.

Prevents 80% of format-related failures

5. Chain-of-Verification (CoVe)

After the model produces an answer, prompt it to generate verification questions about its own output, answer each one, and revise the answer if any verification fails. Particularly effective for fact-sensitive tasks.

↓ hallucination rate by 30–40%

Structured system prompt template

## Role You are a senior financial analyst at a hedge fund. ## Context You analyze SEC filings and earnings call transcripts to identify risks and opportunities for equity positions. ## Constraints - Only reference information explicitly stated in the provided documents - Flag any claim you are uncertain about with "(unverified)" - Never extrapolate beyond what the text supports ## Output format Return a JSON object with: - summary: string (2–3 sentences) - key_risks: list[string] - key_opportunities: list[string] - confidence: float (0–1) ## Example [include 1–2 representative examples here]

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