4 Methods of Prompt Engineering

The video discusses the importance of prompt engineering when interacting with large language models to avoid false results. It introduces four methods: Retrieval Augmented Generation (RAG), which incorporates domain-specific knowledge into the model; Chain-of-Thought (COT), a technique that breaks down complex tasks into simpler steps; ReAct, a few-shot prompting technique that gathers additional information from external sources; and Directional Stimulus Prompting (DSP), which guides the model to provide specific details. The video emphasizes starting with RAG for domain focus and suggests combining techniques like COT and ReAct or RAG and DSP for enhanced results.

Takeaways

• 📚 Prompt engineering is crucial for effectively communicating with large language models by designing proper questions to get desired responses.
• 🚫 Avoiding 'hallucinations' or false results from language models is a key objective, which can occur due to conflicting internet data.
• 🔍 RAG (Retrieval Augmented Generation) involves using a retrieval component to bring domain-specific knowledge to the language model for more accurate responses.
• 💡 The retrieval component can be as simple as a database search, leveraging vector databases to enhance model responses.
• 📈 An example of RAG in use is in the financial industry, where it can provide accurate company earnings by referring to a trusted knowledge base.
• 🤔 COT (Chain-of-Thought) prompts the model through a step-by-step reasoning process, breaking down a complex task into simpler parts.
• 📝 In COT, providing a detailed breakdown of how to approach a problem can lead the model to a more reasoned and accurate conclusion.
• 🔎 ReAct is a few-shot prompting technique that not only reasons through steps but also takes action by sourcing information from external databases when needed.
• 🌐 ReAct differs from RAG in its ability to access public resources in addition to private databases, offering a more comprehensive data set for responses.
• 📊 DSP (Directional Stimulus Prompting) guides the model to focus on specific details within a response, akin to giving hints to draw out particular information.
• 🧠 Combining techniques like RAG, COT, ReAct, and DSP can lead to more refined and accurate interactions with large language models.

Q & A

What is the role of prompt engineering in communicating with large language models?

Prompt engineering is essential for effectively communicating with large language models. It involves designing proper questions to elicit the desired responses from the model, thus avoiding false results or 'hallucinations' that can occur due to the model's training on potentially conflicting internet data.

What does the term 'hallucination' refer to in the context of large language models?

In the context of large language models, 'hallucination' refers to the generation of false or inaccurate information by the model, which can occur because these models are primarily trained on internet data that may contain conflicting or misleading information.

Can you explain the first approach to prompt engineering mentioned in the transcript, RAG or Retrieval Augmented Generation?

RAG, or Retrieval Augmented Generation, is an approach where domain-specific knowledge is integrated with the model to enhance its responses. It works by having two components: a retrieval component that brings the context of the domain knowledge base to the model, and a generated part that responds to queries based on this domain specificity.

How does the retrieval component in RAG work?

The retrieval component in RAG works by searching a database, which could be as simple as a vector database, to bring the context of the domain-specific knowledge base to the larger language model. When a question is asked, this component helps the model to refer to the knowledge base for an accurate response.

What is an example of how RAG could be applied in an industry?

An example of RAG applied in an industry could be a financial company using a large language model to inquire about the total earnings for a specific year. By integrating the company's domain knowledge base into the model, the model can provide an accurate figure rather than an incorrect one based on general internet data.

What is the Chain-of-Thought (COT) approach in prompt engineering?

The Chain-of-Thought (COT) approach involves breaking down a complex task into multiple sections and combining the results of these sections to form the final answer. It requires the model to reason through each step, providing a more detailed and explainable path to the final response.

How does the ReAct (Reasoning and Acting) approach differ from the Chain-of-Thought?

While both ReAct and Chain-of-Thought are few-shot prompting techniques, ReAct goes a step further by not only reasoning through the steps to arrive at a response but also taking action based on additional necessary information. This may involve accessing external or public knowledge bases to gather information not available in the private knowledge base.

What is the ReAct approach's three-step process for handling prompts?

The ReAct approach's three-step process includes: 1) Thought, where the model identifies what information is being sought; 2) Action, where the model retrieves the necessary information from specified sources; and 3) Observation, which is the summary of the action taken to provide the final response.

What is Directional Stimulus Prompting (DSP) and how does it differ from the other techniques?

Directional Stimulus Prompting (DSP) is a technique that guides the large language model to provide specific information by giving it a direction. Unlike other techniques, DSP allows the model to extract particular values or details from a broader task, based on hints provided in the prompt.

How can the different prompt engineering techniques be combined for better results?

Techniques like RAG, which focuses on content grounding, can be combined with COT and ReAct to enhance the model's reasoning and action capabilities. RAG can also be paired with DSP to direct the model towards specific details within the domain content.

What is the importance of avoiding 'hallucinations' when using large language models?

Avoiding 'hallucinations' is crucial because it ensures that the information provided by the large language model is accurate and reliable. False information can lead to incorrect decisions or actions, which can have significant consequences in various applications, especially in sensitive domains like finance or healthcare.

How does the knowledge base play a role in the RAG approach?

In the RAG approach, the knowledge base serves as a source of domain-specific information that the model can reference to provide accurate and relevant responses. It helps in grounding the model's content in the specific domain, thus enhancing the quality of the information provided.

Outlines

00:00

🔍 Introduction to Prompt Engineering

The first paragraph introduces the concept of prompt engineering in the context of large language models. It explains the importance of designing proper questions to communicate effectively with these models to avoid false results, known as 'hallucinations.' The discussion then transitions into different approaches to prompt engineering, starting with Retrieval Augmented Generation (RAG), which involves incorporating domain-specific knowledge into the model to improve responses. The paragraph also touches on the idea of using a knowledge base to provide accurate and domain-specific information to the language model.

05:05

🤖 Chain-of-Thought and ReAct Prompting Techniques

The second paragraph delves into two specific prompt engineering techniques: Chain-of-Thought (COT) and ReAct. COT involves breaking down a complex task into smaller sections and combining the results to form a comprehensive answer. ReAct goes a step further by not only reasoning through the steps but also taking action to gather additional information from external sources when necessary. The paragraph provides an example of how ReAct can be used to retrieve financial data from both private and public databases to answer a query about a company's total earnings for different years.

10:05

📈 Directional Stimulus Prompting and Combining Techniques

The third and final paragraph introduces Directional Stimulus Prompting (DSP), a technique that guides the language model to provide specific information from a task. It is compared to giving hints in a game to achieve a desired outcome. The paragraph concludes with advice on how to combine the different prompt engineering techniques for optimal results, suggesting that RAG should be used first for domain content focus, followed by a combination of COT, ReAct, and DSP as needed.

Keywords

Prompt Engineering

Prompt engineering is the process of designing questions or prompts to effectively communicate with large language models. It involves crafting the right queries to elicit the desired responses from these models. In the video, prompt engineering is essential to avoid 'hallucinations'—false results that can occur when the model is not properly guided. It's about ensuring the model understands the context and provides accurate, relevant information.

Large Language Models

Large language models refer to artificial intelligence systems that are trained on vast amounts of text data from the internet. These models are capable of understanding and generating human-like language. In the context of the video, they are used to perform tasks such as chatbot interactions, summarizing text, and information retrieval. However, they require proper prompts to function accurately.

Hallucinations

In the context of large language models, 'hallucinations' refer to the generation of false or incorrect information by the model. This can happen when the model is not provided with the right context or specific enough prompts. The term is borrowed from the concept of hallucination in humans, where perceptions are experienced without any external stimuli, and is used metaphorically here to describe inaccurate outputs.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is one of the four approaches to prompt engineering discussed in the video. It involves combining a retrieval system, which searches a knowledge base for relevant information, with a generative model. The knowledge base provides domain-specific context to the model, allowing it to generate more accurate responses. An example given in the video is using RAG to find the correct annual earnings of a company by referring to a trusted source within the company's knowledge base.

Chain-of-Thought (COT)

Chain-of-Thought (COT) is a prompt engineering technique where a complex question is broken down into simpler, more manageable steps. The model is guided through these steps to reach the final answer. It's akin to explaining a concept as if to an 8-year-old, simplifying the process into understandable parts. In the video, COT is used to calculate the total earnings of a company by breaking down the earnings into different business segments and then summing them up.

ReAct

ReAct is a few-shot prompting technique that involves not just reasoning but also taking action based on what is necessary to arrive at the response. Unlike Chain-of-Thought, which focuses on breaking down the steps to reach an answer, ReAct can access external resources to gather the required information. In the video, it is exemplified by a scenario where the model retrieves earnings data for different years from both a private and a public knowledge base to provide a comprehensive answer.

Directional Stimulus Prompting (DSP)

Directional Stimulus Prompting (DSP) is a technique where the model is given a specific direction to focus on particular aspects of the information requested. It's like providing hints to guide the model towards extracting specific details. For instance, if one wants to know the annual earnings of a company with a focus on software and consulting, DSP would guide the model to provide detailed earnings for those specific areas.

Content Grounding

Content grounding is the concept of making a large language model aware of specific domain content or context. This is a crucial step in prompt engineering, especially when using RAG, to ensure that the model's responses are relevant and accurate to the user's domain. It helps in narrowing down the model's focus to the specific knowledge base that contains the required information.

Vector Database

A vector database is a type of database that stores and manages data in the form of vectors, which are mathematical objects representing points in space. In the context of the video, a vector database can be used as part of the retrieval component in RAG to efficiently search through a knowledge base and bring relevant domain-specific information to the model.

Few-Shot Prompting

Few-shot prompting is a technique in prompt engineering where the model is provided with a few examples to guide its responses. This approach helps the model to learn from the given examples and improve the quality of its output. Both Chain-of-Thought and ReAct are mentioned as few-shot prompting techniques in the video, each with a slightly different application.

Knowledge Base

A knowledge base is a repository of information that is structured and organized in a way that allows for easy retrieval, often specific to a particular domain or industry. In the video, knowledge bases are used in RAG and ReAct to provide domain-specific data to the language model, ensuring that the responses are accurate and relevant to the user's context.

Highlights

Prompt engineering is crucial for effectively communicating with large language models by designing proper questions to avoid false results.

Large language models are predominantly trained on Internet data, which can contain conflicting information.

RAG (Retrieval Augmented Generation) involves combining domain-specific knowledge with a model to improve responses.

In RAG, a retrieval component brings the context of a domain knowledge base to the language model's generated part.

A simple example of a retriever could be a database search or a vector database.

COT (Chain-of-Thought) involves breaking down a complex task into sections and combining results for a final answer.

ReAct is a few-shot prompting technique that not only reasons through steps but also acts based on additional necessary information.

ReAct can access both private and public knowledge bases to gather information and form a response.

DSP (Directional Stimulus Prompting) guides the language model to provide specific information by giving hints.

Combining RAG with COT and ReAct or DSP can enhance the effectiveness of prompt engineering techniques.

Hallucinations in language models refer to false results due to the model's reliance on potentially inaccurate internet data.

Content grounding is a key aspect of working with large language models, emphasizing the importance of domain-specific knowledge.

An example of RAG in action is using a financial company's knowledge base to provide accurate earnings figures.

In Chain-of-Thought, the language model reasons through a problem, providing a step-by-step breakdown leading to the final answer.

ReAct differs from RAG in its ability to retrieve and utilize information from external sources to enhance responses.

The ReAct process involves thought, action, and observation steps to guide the language model towards a comprehensive answer.

DSP allows for the extraction of specific details from a broader query, focusing the language model's response on particular areas of interest.