Step 5: Defining LLM Prompt

Now it’s time to refine the conversation flow by defining a clear “prompt” for the LLM.

The Role of the Prompt:

The prompt for the Large Language Model (LLM) can be described as the input or the question given to the model to generate a response. It's the specific text or instructions you provide to the model to prompt it to produce output.

In case of chatbot, the prompt acts like a specific instruction for the LLM, guiding it on how to use the retrieved website content (documents) to answer the user’s query effectively. A well-crafted prompt can significantly improve the quality and relevance of the chatbot’s responses.

Crafting an Effective Prompt:

Here are some key elements to consider when writing your prompt:

Introduce the User Query: Clearly state the user’s question within the prompt.
Reference the Retrieved Documents: Indicate that the LLM should consider the documents retrieved from the Chroma vector store based on the user’s query.
Guide the Response Generation: Provide instructions on how the LLM should utilize the documents to answer the user. This could involve summarizing relevant information, extracting key points, or directly answering the question using the retrieved content.

Here’s a basic example prompt structure:

"The user asked '{user_query}'.  Based on the retrieved documents {documents}, please provide a comprehensive and informative answer to their question."

Fine-Tuning the Prompt:

You can experiment with different prompt structures and wording to achieve the desired conversational style and response quality for your chatbot.

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Project - Building a RAG Chatbot from Your Website Data using OpenAI, Langchain and Vector Database

Step 5: Defining LLM Prompt

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