Master Prompt Engineering framework - Harness the Power of LLMs

Unlock 10x productivity with the top five prompt engineering frameworks

The world has no lack of LLMs given the choices that we have; quite frankly, These models have been commoditized as we speak, and it is accelerating, e.g. refer to my last article OpenAI GPT4o, Meta Llama 3.1B, Google Gemini 1.5 pro. Apps such as ChatGPT and Perplexity powered by LLMs will become everyday tools similar to MS Word and Excel.

Therefore, what matters the most to a working professional is how we derive value from these models and how you get these apps to work for you at their best. That comes to an essential technique that every IT professional needs to master → prompt engineering.?

What makes a good prompt??

To harness the power of large language models (LLMs) effectively, a good prompt should be clear, specific, and well-structured. Here are the key elements that make a good prompt?

1. Clarity and Specificity

Clear Instructions: The prompt should provide unambiguous instructions to avoid multiple interpretations.

Specific Details: Include detailed information about the context, desired outcome, format, style, and length.

• Context and Background

Relevant Context: Provide sufficient background information to help the model understand the scenario.

Audience Specification: Tailor the prompt to the target audience to ensure the output is relevant.

• Desired Output Format

Examples: Use examples to illustrate the expected output format.

Structured Format: Clearly define the structure of the response, such as bullet points, lists, or paragraphs.

• Iterative Refinement

Feedback Mechanism: Allow for iterative improvements based on the model's responses.

Experimentation: Test different prompt variations to identify the most effective ones.

• Avoiding Ambiguity

Precise Language: Minimize the use of vague or "fluffy" descriptions.

Explicit Instructions: Clearly state what to do and what not to do.

How Frameworks Make Prompts Effective

The prompt Engineering framework provides a structure to help you construct a more effective prompt. Over 40 prompt engineering frameworks have been developed over the past two years, which is quite a pain to follow. Fundamentally, Evaluating a prompt engineering framework involves assessing its effectiveness, usability, iterative improvement capabilities, performance metrics, community support, and adaptability.

I recommend the following five frameworks based on ease of use, output performance, use case, prompt re-usability.?

CRISP Framework?

• Context: Provides a detailed background, helping the model understand the scenario.
• Request: Specifies the task clearly, ensuring the model knows what to do.
• Insight: Adds additional information, enhancing the model's understanding.
• Style: Defines the tone and manner of the response, making it suitable for the target audience.
• Parameters: Sets boundaries for the response, such as length and format, ensuring the output meets specific requirements.

ERA Framework?

• Expectation: Outlines the desired outcome, providing clear goals for the model.
• Role: Specifies the role of the model, guiding its behaviour.
• Action: Describes the specific task, ensuring the model knows what action to take.

RTF Framework?

• Request: Clearly defines the user's request.
• Task: Specifies the task to be performed.
• Format: Outlines the desired format of the response, ensuring clarity and structure.

Chain-of-Thought (CoT) Framework?

• Step-by-Step Reasoning: Encourages the model to think through the problem step-by-step, improving the quality of complex reasoning tasks.
• Iterative Process: Allows for refinement and feedback, enhancing the model's performance over time.

LangGPT Framework?

LangGPT is a prompt engineering framework Inspired by structured reusable programming languages that make NLP prompt more structured and reusable.?

• Template-Based: Uses templates, variables, and commands to create structured prompts, making it easier to generate high-quality outputs.
• Dual-Layer Structure: Allows for reusable and modular prompts, enhancing efficiency and consistency.
• Community Support: Provides shared templates and resources, fostering collaboration and continuous improvement.

The basic module includes the following elements:

It was created by a China-based community and has demonstrated better performance in guiding LLMs to perform tasks compared to other prompt engineering frameworks

The Pros and Cons of Each Framework with a Scenario, and The ideal use case for different framework.

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