Unlocking the Power of Generative AI: Demystifying LLM Prompting Techniques

Photo by Steve Johnson on Unsplash

Introduction: Setting the Stage for LLM Prompting

In the realm of artificial intelligence, generative language models (LLMs) have emerged as powerful tools for creative text generation, translation, and even writing different kinds of creative content. However, these models operate on vast amounts of data and require careful guidance to produce meaningful outputs. This is where LLM prompting techniques come into play.

LLM prompting involves crafting instructions that effectively communicate our desired task to these sophisticated models. These prompts serve as a bridge between human intent and the model’s intricate knowledge base, enabling them to generate text that aligns with our expectations.

Photo by Mojahid Mottakin on Unsplash

The Essence of Inference: Guiding LLMs through Language

At the heart of LLM prompting lies the concept of inference. Inference involves using the model’s knowledge base to deduce the meaning of our prompts and produce appropriate responses. This process is similar to how humans interpret language, making it a crucial aspect of effective communication with LLMs.

By providing clear and concise prompts, we guide the model’s inference process, ensuring that it understands our intentions and generates text that aligns with our expectations. This is where the art of prompt engineering comes into play.

By using inference (just asking) the LLM with a question as seen in the picture surrounded by a red square, we will get generic answers derived from the models training data.
What should be noted when using inference is that the absence of question-specific context makes this technique more prone to hallucination.

Prompt Engineering: Crafted Instructions for LLMs

Prompt engineering is the process of meticulously crafting prompts that effectively communicate our desired outcomes to LLMs.

This realisation often comes with inspiration from Generative AI Cheat Sheets that is shared frequently.

Effective prompts are typically characterized by clarity, conciseness, and context. They should be free of ambiguity and provide specific instructions that steer the model’s attention. Additionally, providing context and relevant examples can significantly enhance the model’s understanding and output.

There is a lot of different guidelines of what to include in a prompt, but here is some of the main points:

• Persona
• Output length
• Output format
• Targeted audience
• Tone of voice

By adding the structure (orange squares) around the question (red squares) as the prompt engineering provides we will get a more tailored made answer that fits our use case. Even though we will still get answers that is derived from the models training data

Retrieval Augmented Generation: Bridging the Gap between Text and Code

Retrieval augmented generation (RAG) is an advanced prompting technique that combines the strengths of LLMs with traditional information retrieval methods. This approach allows models to draw upon a vast repository of text and/or code, effectively bridging the gap between these two domains.

Using RAG, we move complexity to the system but the complexity mainly resides outside of the LLM prompting. As RAG requires relevant information to be added to the question, it is important that the process of fetching information provides accurate and relevant information, some points to consider:

• Well structured data
• Easily accessible for searching
• Know where to look

By incorporating RAG techniques, we can guide LLMs to generate text that is not only creative and original but also grounded in factual information and relevant sources. This capability has significant implications for tasks such as research, summarisation, and creative writing.

In this method we are adding factual information that are specific to our company and our personal business, meaning that we do not longer need to rely on the LLM training.

Other methods for LLM interactions:

Chain of Thought (REACT), is a prompting technique that breaks down complex tasks into a sequence of smaller, more manageable prompts. This modular approach allows for greater control over the model’s reasoning process and ensures that it follows the intended logic.

Fine Tuning, is the process of further training an already pre-trained LLM on a smaller, task-specific, labeled dataset. In this way, we adjust some of the model parameters to optimize its performance for a particular task or set of tasks.

Re-Training, is the process of training an LLM from scratch on a new dataset. This can be done to improve the model’s performance on a particular task, or to update the model with new information.

Summary and Conclusion

The next time you interact with LLMs, use a combination of the described techniques:

• Inference
• Prompt Engineering
• Retrieval Augmented Generation

Instead of prompting:

Generate a response to my colleagues email asking on the status on my work for Project MediumArticle

Use the described techniques and prompt:

Acting as a ambitious software engineer. Generate a response to my colleagues email:
Hi Fredrik,
I wanted to check in on your progress with Project MediumArticle. I know you’re working on the article “Unlocking the Power of Generative AI: Demystifying LLM Prompting Techniques”, and I’m just curious to see where you’re at.
Do you have a timeline for completing this task? Is there anything I can do to help you out?
I’m also checking in because we have a deadline coming up, and I want to make sure that we’re on track to meet our goals.
My current status is that I will be done within one working day. Provide a concise and friendly email, with a positive writing style.

By combining prompt engineering and retrieval augmented generation, we can create more effective prompts that will allow LLMs to perform more complex tasks and generate more accurate and relevant results. In the end making us more productive with the tools we have at hand.

Reference Materials: Links that Influenced this Article

• Youtube IBM Technology — How Large Language Models Work (RAG)
• Youtube IBM Technology — Why Large Language Models Hallucinate
• Medium — Prompt Engineering Cheat Sheet
• OpenAI — Prompt engineering
• OpenAI — Best practices for prompt engineering with OpenAI API
• Microsoft — Introduction to prompt engineering
• Youtube IBM Technology — What is Retrieval-Augmented Generation (RAG)
• Prompt Engineering Guide — Retrieval Augmented Generation (RAG)