4 Methods of Prompt Engineering
TLDRThe video script discusses the importance of prompt engineering in effectively communicating with large language models to avoid false results, also known as 'hallucinations'. It introduces four methods of prompt engineering: Retrieval Augmented Generation (RAG), which uses domain-specific knowledge to improve responses; Chain-of-Thought (COT), a method that breaks down tasks into smaller sections for more precise answers; ReAct, a technique that goes beyond reasoning to gather information from external sources when necessary; and Directional Stimulus Prompting (DSP), which guides the language model to provide specific information from a task. The video emphasizes the need to first use RAG for content grounding, and then suggests combining COT, ReAct, and DSP for enhanced outcomes.
Takeaways
- ๐ Prompt engineering is crucial for effectively communicating with large language models to avoid false results.
- ๐ Large language models are primarily trained on Internet data, which may contain conflicting information.
- ๐ RAG (Retrieval Augmented Generation) enhances model responses by incorporating domain-specific knowledge bases.
- ๐ก The retrieval component in RAG brings domain knowledge context to the language model's generated responses.
- ๐ A simple database search, like a vector database, can act as a retriever in RAG to provide accurate industry-specific information.
- ๐ค COT (Chain-of-Thought) involves breaking down complex tasks into smaller sections and combining results for a comprehensive answer.
- ๐ By using COT, you guide the language model through a series of prompts, leading to a reasoned and explainable response.
- ๐ ๏ธ ReAct is a few-shot prompting technique that not only reasons through steps but also acts based on additional necessary information from various sources.
- ๐ ReAct differentiates from RAG by leveraging both private and public knowledge bases to gather comprehensive data for responses.
- ๐ DSP (Directional Stimulus Prompting) is a technique that directs the language model to provide specific details from a broader query.
- ๐ง Combining RAG, COT, ReAct, and DSP can cumulatively improve the quality and specificity of responses from large language models.
Q & A
What is prompt engineering in the context of large language models?
-Prompt engineering is the process of designing and formulating proper questions to effectively communicate with large language models. It aims to get the desired responses from these models while avoiding false results or 'hallucinations' that may arise from the models' training on potentially conflicting internet data.
Why is it important to avoid hallucinations when using large language models?
-Hallucinations refer to the generation of false or inaccurate information by large language models. It is important to avoid them because they can lead to incorrect outputs that may misinform users or lead to incorrect decisions based on the model's responses.
What is RAG, and how does it work in the context of prompt engineering?
-RAG stands for Retrieval Augmented Generation. It is a method where domain-specific knowledge is added to a model to improve its responses. This involves a retrieval component that brings the context of a domain knowledge base to the language model, allowing it to respond to queries based on the specificity of the content in the knowledge base.
Can you provide an example of how RAG is applied in an industry?
-An example of RAG in the financial industry could involve querying a large language model for a company's total earnings for a specific year. By directing the model to refer to a trusted domain knowledge base, the model can provide an accurate figure, as opposed to potentially inaccurate information derived from general internet data.
What is the Chain-of-Thought (COT) approach in prompt engineering?
-The Chain-of-Thought approach involves breaking down a complex task into multiple sections or steps. The large language model is guided through these steps to combine the results and arrive at a final answer. This method helps the model reason through the problem and provides a more detailed and explainable response.
How does the ReAct approach differ from the Chain-of-Thought approach?
-While both ReAct and Chain-of-Thought are few-shot prompting techniques, ReAct goes a step further by not only reasoning through the steps but also taking action based on additional necessary information. ReAct can access external resources, such as public knowledge bases, to gather information that is not available in the private knowledge base.
What is the ReAct approach's three-step process for handling prompts?
-The ReAct approach involves splitting the prompt into three steps: thought, action, and observation. The thought step defines what information is being sought. The action step involves the model going to the appropriate knowledge base to retrieve the needed information. The observation step summarizes the action taken and provides the retrieved value.
What is Directional Stimulus Prompting (DSP), and how does it help in obtaining specific information?
-Directional Stimulus Prompting is a technique where the model is given a hint or direction to focus on specific details within a task. For example, when asking about a company's annual earnings, DSP can guide the model to extract and provide specific values for certain sectors, like software or consulting, rather than just the overall figure.
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 and DSP can also be combined to direct the model towards specific information within the domain content, creating a cumulative effect that improves the quality of the responses.
Why is it recommended to start with RAG when using multiple prompt engineering techniques?
-Starting with RAG ensures that the large language model is made aware of the domain-specific content, providing a solid foundation for further refinement using other techniques like COT, ReAct, or DSP. This initial step of content grounding is crucial for generating accurate and relevant responses.
How can prompt engineering techniques help in improving the accuracy of financial data retrieval from large language models?
-By using prompt engineering techniques like RAG, which integrates domain knowledge bases, or ReAct, which can access both private and public databases, the model can provide more accurate financial figures. These techniques help the model avoid generating false numbers and instead rely on trusted and specific data sources to answer financial queries.
Outlines
๐ Introduction to Prompt Engineering
The first paragraph introduces the concept of prompt engineering in the context of large language models (LLMs). It discusses the importance of designing proper questions to communicate effectively with LLMs to avoid false results, known as 'hallucinations'. The conversation outlines four different approaches to prompt engineering, starting with Retrieval Augmented Generation (RAG). RAG involves incorporating domain-specific knowledge into the model to improve the accuracy of responses. The discussion also touches on the limitations of LLMs, which are primarily trained on Internet data that can contain conflicting information.
๐ค Chain-of-Thought (COT) and ReAct Prompting Techniques
The second paragraph delves into the Chain-of-Thought (COT) approach, which involves breaking down complex tasks into smaller sections and combining the results to form a comprehensive answer. This method is compared to explaining a concept to an 8-year-old, emphasizing the need for clear and guided prompts. The paragraph also introduces the ReAct technique, which extends beyond reasoning to include actions based on necessary information. ReAct can access both private and public knowledge bases to gather the required data. The difference between RAG and ReAct is highlighted, with RAG focusing on content grounding and ReAct on gathering additional information from external sources.
๐ Directional Stimulus Prompting (DSP) and Combining Techniques
The third paragraph introduces Directional Stimulus Prompting (DSP), a method that guides the LLM to provide specific information by giving hints about the desired details. This technique is likened to providing hints in a game to achieve a better result. The paragraph concludes with a discussion on how to combine different prompt engineering techniques for optimal results, suggesting starting with RAG to focus on domain content and then combining COT, ReAct, and DSP for a cumulative effect. The conversation ends with an invitation to continue learning about prompt tuning in future episodes.
Mindmap
Keywords
๐กPrompt Engineering
๐กLarge Language Models (LLMs)
๐กHallucinations
๐กRetrieval Augmented Generation (RAG)
๐กChain-of-Thought (COT)
๐กReAct
๐กDirectional Stimulus Prompting (DSP)
๐กContent Grounding
๐กFew-Shot Prompting
๐กKnowledge Base
๐กVector Database
Highlights
Prompt engineering is crucial for effective communication with large language models.
It involves designing proper questions to get desired responses and avoid false results.
Large language models are trained on Internet data, which may contain conflicting information.
RAG (Retrieval Augmented Generation) is the first approach discussed, involving domain-specific knowledge.
RAG works by combining a retrieval component with a large language model to provide domain-specific responses.
The retrieval component can be as simple as a database or vector database search.
An example of RAG is using a company's financial knowledge base to get accurate earnings figures.
COT (Chain-of-Thought) is the second approach, which breaks down a task into multiple sections.
COT involves guiding the model through prompts to get the desired response.
ReAct is a few-shot prompting technique that goes beyond reasoning to acting based on necessary information.
ReAct can access both private and public knowledge bases to gather information for responses.
Directional Stimulus Prompting (DSP) is a newer technique that guides the model to provide specific information.
DSP works by giving hints to the model, similar to providing clues in a game.
Combining RAG, COT, ReAct, and DSP can yield a cumulative effect for more effective prompt engineering.
Content grounding is a key aspect of working with large language models, emphasized in both RAG and ReAct.
The ReAct approach involves a 3-step process: thought, action, and observation.
Hallucinations refer to false results generated by large language models due to conflicting training data.
Prompt engineering aims to minimize hallucinations by structuring queries for accurate information retrieval.
Different prompt engineering techniques can be combined for more precise and effective communication with language models.