Advanced Prompt Engineering with Chain-of-Thought and Function Calling
Advanced prompt engineering techniques to maximize LLM performance in production environments.
Master prompt engineering techniques that improve the quality, consistency, and reliability of LLM responses in production applications.
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Prompt objective
Master prompt engineering techniques that improve the quality, consistency, and reliability of LLM responses in production applications.
Real use case
A Santa Catarina-based healthtech called SaudePlus uses GPT-4 for symptom triage, but 15% of responses are inconsistent and 8% contain information contradicting established medical guidelines. They need more robust prompts to ensure safety and consistency.
Customize these fields first
Replace the placeholders with your own context before you run the prompt. That usually improves the first output more than adding more instructions later.
Prompt
Develop an advanced prompt system for [PROJECT NAME], which uses [GPT-4/CLAUDE/LLAMA] for [USE CASE] via API. **Context:** - Use case: [DETAILED DESCRIPTION] - Volume: [NUMBER] API calls per day - Quality requirements: [DESCRIBE: accuracy, consistency, tone, etc.] - Current cost: $[AMOUNT]/month with [CURRENT MODEL] - Current problems: [DESCRIBE: inconsistency, hallucinations, etc.] **1) System Prompt Engineering:** - Persona and role definition (who is the assistant) - Explicit constraints (what it should NEVER do) - Output format specification (JSON schema, markdown structure) - Tone and language guidelines (formal/informal) - Knowledge boundaries (
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How to use this prompt
- 1Replace the key placeholders first: PROJECT NAME, GPT-4/CLAUDE/LLAMA, USE CASE, DETAILED DESCRIPTION.
- 2Replace any bracketed placeholders like [this] with your own context.
- 3Add extra background information when you want more tailored results.
- 4Combine multiple prompts in one conversation when you need a richer output.
- 5Save your best-performing prompts so they are easy to reuse later.
Next best step
Open the guide first, then branch only if you still need more.
A guide for technical builders choosing between prompts, coding workflows, and agent-based implementation.
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