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Meta Llama Prompt Engineering Guide

Official best practices for prompting Meta's Llama models, including Llama 3 and Code Llama

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TL;DR

•Use explicit instructions with clear formatting
•Combine roles, rules, and examples in prompts
•Leverage few-shot and chain-of-thought techniques
•Use RAG for fact-based applications
•Iterate and refine prompts based on outputs
•Test across diverse scenarios for reliability

Key Principles

1Be explicit about format, style, and constraints

2Assign clear roles to guide response style

3Use examples to demonstrate desired output

4Apply chain-of-thought for reasoning tasks

5Limit extraneous tokens with precise instructions

6Use retrieval-augmented generation (RAG) for factual accuracy

Explicit Instructions

Llama models perform best with explicit, detailed instructions. Clearly specify the format, style, length, and any constraints for the output you want.

Examples

Specify format explicitly

Bad:

List some programming languages

Good:

List 5 programming languages suitable for beginners.
Format: Numbered list
For each language include:
- Name
- One sentence on why it's beginner-friendly
- One popular use case

Explicit formatting instructions ensure consistent, structured output.

Role Assignment

Assigning a role to Llama helps guide its responses toward the expertise and tone appropriate for your use case. Combine roles with specific rules for best results.

Examples

Combine role with rules

Good:

You are a senior Python developer conducting a code review.

Rules:
- Focus on code quality and best practices
- Be constructive, not harsh
- Suggest specific improvements
- Prioritize security issues

Review this code:
```python
def get_user(id):
    return db.query(f"SELECT * FROM users WHERE id = {id}")
```

#prompt-engineering#llama#meta-ai#few-shot#chain-of-thought#rag#code-llama

Last updated: December 1, 2024

Few-Shot Learning

Provide one or more examples of the desired behavior. This helps Llama understand the pattern you want, especially for formatting or domain-specific tasks.

Examples

Show input-output pattern

Good:

Convert informal text to professional email language.

Example:
Input: "hey can u send me that report asap thx"
Output: "Hello, could you please send me the report at your earliest convenience? Thank you."

Now convert:
Input: "gonna need those numbers before the meeting tmrw"

The example establishes both the task and the desired tone transformation.

Chain-of-Thought Prompting

For complex reasoning or multi-step problems, ask Llama to think through the problem step by step before providing the final answer.

Examples

Step-by-step reasoning

Good:

Think through this problem step by step:

A store has a 20% off sale. If an item originally costs $80 and you have a $10 coupon that applies after the discount, how much do you pay?

Show your reasoning, then give the final answer.

Explicit step-by-step instruction improves accuracy on math and logic problems.

Retrieval-Augmented Generation (RAG)

For fact-based applications, include retrieved information directly in the prompt. This is more cost-effective than fine-tuning and ensures up-to-date information.

Examples

Include retrieved context

Good:

Use ONLY the following information to answer the question. If the answer is not in the provided text, say "I don't have enough information."

<retrieved_documents>
{Insert relevant documents here}
</retrieved_documents>

Question: {User's question}

Constraining to retrieved documents reduces hallucination in knowledge-intensive tasks.

TL;DR

Key Principles

Explicit Instructions

Examples

Role Assignment

Examples

Few-Shot Learning

Examples

Chain-of-Thought Prompting

Examples

Retrieval-Augmented Generation (RAG)

Examples

Iterative Refinement