Hello guys and welcome to the AI Insider. In today’s video, we’ll reveal the secrets of prompt engineering in AI. By the end of this video, you will have a solid understanding of prompt engineering and how it can improve the performance of language models.
Let’s start by discussing the importance of context in prompt engineering. When using language models like GPT, it’s crucial to provide the model with the necessary context to generate accurate and relevant responses.
For example, let’s say you want to know the best restaurant in Delhi. A simple prompt like ‘What is the best restaurant?’ lacks context and doesn’t provide any information about the location or theme of the restaurant. As a result, the model may give a generic or irrelevant response.
However, if you modify the prompt to ‘What is the best restaurant in Delhi with a classic theme?’, you provide the necessary context for the model to give a more accurate response. This context helps the model narrow down the search to restaurants in Delhi with a classic theme.
Research shows that providing context improves the performance of language models. In a study on situated QA, it was found that 65.5% of natural questions required context to generate accurate answers.
Context learning plays a crucial role in prompt engineering. It involves two phases: chunking/indexing and querying. In the chunking phase, documents are divided into chunks and fed to the language model to generate embeddings. These embeddings are then stored in a vector database. In the querying phase, a user query is converted into an embedding and searched in the vector database to retrieve relevant information.
The length of the context is important, but it’s also essential to provide an efficient context. Overwhelming the model with too much information can lead to suboptimal results. It’s important to strike a balance between providing enough context and avoiding information overload.
Prompt engineering is the process of constructing prompts that provide efficient context to language models. By practicing prompt engineering, you can improve the performance of language models like GPT and ensure more accurate and relevant responses.
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