Unlocking Super Powers of Large Language Models

The design is from Google

By now, you are familiar with large language models (LLMs) and use them daily. These models excel at generating text, translating languages, answering questions, and performing other language-related tasks. However, to fully harness the potential of LLMs, it is crucial to understand and apply effective techniques for refining and enhancing their output. This article will explore three key techniques: prompt engineering, retrieval-augmented generation (RAG), and fine-tuning.

Prompt Engineering: Directing the Model’s Behavior

Prompt engineering involves providing specific instructions and context to an LLM through a “prompt”. This prompt serves as a guide, influencing the model’s output by directing its attention to relevant information, setting the desired tone or style, and defining the expected structure and format of the response.

Prompt engineering is a straightforward and accessible technique that allows users to rapidly prototype and experiment with different approaches. It provides intuitive control over the model’s behaviour, enabling the creation of outputs tailored to specific requirements.

Example Prompt and Response

Let’s say you want to use prompt engineering to get an LLM, like Chatgpt, Gemini, to generate a summary of a complex research article. Here’s how you might craft your prompt to achieve this:

You are a scientific researcher with expertise in summarizing complex research articles. Summarize the following article in a concise and clear manner, highlighting the key findings, methodology, and implications of the research. Use a formal tone and ensure the summary is understandable to other researchers in the field.

Article:
The study investigates the effects of a new drug on the progression of Alzheimer’s disease. The researchers conducted a double-blind, placebo-controlled trial involving 200 participants over a period of 18 months. Key findings include a significant reduction in the rate of cognitive decline in the treatment group compared to the placebo group. The study used neuroimaging and biomarker analysis to support these findings. The implications suggest that this drug could potentially slow the progression of Alzheimer’s disease, providing a new avenue for treatment.

Please provide a summary of this article.

Retrieval Augmented Generation (RAG): Incorporating External Knowledge

Imagine you want to build a chatbot for your company that allows customers to access details about your services or products. Simply integrating a large language model (LLM) won’t suffice, as it lacks access to your specific data. Even though you have the necessary information, it’s too extensive to fit into a single prompt due to the limited context window of LLMs. What if you could extract only the relevant data needed to answer a customer’s query and feed that to the LLM? This is precisely what a Retrieval-Augmented Generation (RAG) system does.

RAG takes prompt engineering a step further by incorporating external knowledge into the prompt. This knowledge can come from various sources, such as databases, documents, or online resources. By retrieving and utilizing relevant information from these sources, RAG enhances the model’s response with facts, specific details, and up-to-date information.

Including external knowledge through RAG grounds the model’s responses in reality, ensuring accuracy and reducing the risk of hallucinations or biased outputs. It also allows the model to access and process dynamic information in real time, making it adaptable to changing scenarios.

A typical RAG system contains several key components:

1. Data Splitting: This component divides the extensive company data into manageable chunks or segments that can be more easily processed.
2. Embedding: Each chunk of data is converted into dense vector representations, or embeddings, which capture the semantic meaning of the text.
3. Storage: The embeddings are stored in a database optimized for fast retrieval, such as a vector database.
4. Search Mechanism: When a customer query is received, the system searches through the stored embeddings to find the most relevant data.
5. Relevance Scoring: The retrieved data is ranked based on its relevance to the query, ensuring the most pertinent information is prioritized.
6. Response Generation: The LLM uses the top-ranked, relevant data to generate a coherent and accurate response to the customer’s query.
7. Feedback Loop: This component gathers feedback on the chatbot’s responses to continuously improve the system’s accuracy and relevance.

source: Heiko Hotz

Fine-Tuning: Customizing the Model for Specific Tasks

Fine-tuning involves training an LLM on a custom dataset of prompt-completion pairs. These pairs demonstrate the desired input-output behaviour for the model. Fine-tuning enables the model to learn and adapt to specific tasks and domains by iteratively adjusting the model's parameters based on the training data.

Fine-tuning is particularly effective in situations where the default behaviour of the LLM is insufficient or where additional customization is required. It allows users to “teach” the model-specific knowledge, skills, or writing styles, resulting in outputs aligned with the intended purpose.

source: Heiko Hotz

Combining Techniques

Prompt engineering, RAG, and fine-tuning are not mutually exclusive techniques. We can be combine them to achieve optimal performance.

• By combining prompt engineering with RAG, users can provide the model with both specific instructions and relevant external knowledge, enhancing the output’s accuracy and specificity.
• Integrating RAG and fine-tuning enables the model to learn from custom data while also incorporating external knowledge, resulting in outputs that are both tailored and informed.
• Combining prompt engineering and fine-tuning allows users to fine-tune the model’s behaviour for specific tasks while also controlling the prompt’s content and structure.

By understanding and utilizing these techniques, developers and practitioners can effectively harness the power of LLMs to generate highly relevant, accurate, and customized outputs, unlocking a wide range of applications in various domains.

In the next article, we’ll explore building a basic RAG system using Langchain. Be sure to also check out my other articles. See you in the next one, and until then, stay safe! ✌️

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References:

[1]. RAG vs Finetuning.[2024] Docs [Online]. Available at: https://towardsdatascience.com/rag-vs-finetuning-which-is-the-best-tool-to-boost-your-llm-application-94654b1eaba7 [Accessed 07 June2024]