Welcome to the final chapter of our Docs-Reader tutorial series! We've come a long way. We learned how to store document data efficiently in a vector database using numerical Text Embedding of document Text Splitting. We then put all these pieces together in the Data Ingestion pipeline to prepare our documents for search. Finally, in Query preprosseing, we saw how the system receives a user's question, finds relevant document chunks in the database (Retrieval), and uses those chunks to generate an answer with an AI model (Generation).

But how exactly do we tell the AI model to use only the document chunks we give it and answer only the user's specific question? Sending just the chunks and the question isn't enough. We need to give the AI clear instructions.

What Problem Does Prompt Engineering Solve?

Imagine you've retrieved the perfect paragraphs from your documents that contain the answer to a user's question. You have the user's question and these relevant document snippets. Now you need to give these to a powerful AI language model and ask it to synthesize an answer.

If you just give the AI the chunks and the question without guidance, it might:

• Ignore the chunks entirely and answer from its general training data (which might be wrong or outdated).
• Include information not present in the chunks.
• Fail to structure the answer correctly.
• Get confused if the chunks contain slightly conflicting information.

The core problem is: How do we effectively instruct an AI language model to perform a specific task (answer a question) using only provided information (retrieved document chunks)?

This is where Prompt Engineering comes in.

Enter Prompt Engineering

Prompt Engineering is the art and science of crafting the input (the "prompt") you give to an AI language model to guide its behavior and get the desired output.

In the context of our Docs-Reader project, prompt engineering involves designing a specific message structure that includes:

1. Clear Instructions: Telling the AI what to do (e.g., "Answer the question...", "Use only the following context...", "If you cannot find the answer...").
2. The Context: Providing the relevant document chunks retrieved from the vector database.
3. The User's Query: Including the original question the user asked.

Think of it like giving a master chef a very specific recipe:

• The Chef: This is the powerful AI language model. It knows how to cook (generate text), but it needs direction.
• The Ingredients: These are the retrieved document chunks – the specific information the chef must use.