You have now successfully retrieved document chunks that are semantically relevant to the user's query. The next significant step in the Retrieval Augmented Generation (RAG) process is to combine this retrieved information with the original query and present it effectively to the Large Language Model (LLM). Simply performing the retrieval isn't enough; the LLM needs to be explicitly instructed on how to use this newfound context to generate its answer.

The Role of Prompt Engineering in RAG

At its heart, combining context and query involves careful prompt engineering. We need to construct a prompt that guides the LLM to prioritize the provided context when formulating its response. The goal is to make the LLM's generation conditional on the retrieved documents, rather than solely relying on its internal, pre-trained knowledge.

Structuring the Augmented Prompt

A common approach is to create a prompt template that incorporates placeholders for both the retrieved context and the original user query. The structure often looks something like this:

Instructions: Tell the LLM its task and how it should use the provided context.
Context: Insert the content of the retrieved document chunks.
Query: State the user's original question or request.

Here's a conceptual example using a Python f-string to illustrate the structure:

# Assume 'retrieved_chunks' is a list of strings (document content)
# Assume 'user_query' is the original question string

# Combine the chunks into a single string, often with separators
formatted_context = "\n\n---\n\n".join(retrieved_chunks)

# Create the final prompt
prompt = f"""
You are an assistant designed to answer questions based *only* on the provided documents.
Do not use any information outside of the context given below.
If the answer cannot be found in the documents, state that clearly.

Context Documents:
{formatted_context}

User Query:
{user_query}

Answer:
"""

# This 'prompt' string would then be sent to the LLM API.

Formatting the Retrieved Context

How you format the formatted_context part is important. Simply concatenating text might be confusing for the LLM. Common strategies include:

Separators: Using clear separators like ---, ***, or specific markers ([DOCUMENT 1 START]...[DOCUMENT 1 END]) between chunks.
Numbering: Prefixing each chunk with an identifier (e.g., Document 1: ..., Document 2: ...).
Metadata: Sometimes including source information (like filenames or page numbers) if available and relevant.

The choice depends on the specific LLM and experimentation. The aim is to make it unambiguous where one piece of retrieved information ends and another begins, and to clearly distinguish the context section from the instructions and the user query.

Crafting Effective Instructions

The initial instruction section of the prompt is highly influential. It sets the stage for how the LLM should behave. Consider these variations:

Strict Context Adherence: "Answer the following question using only the information provided in the documents below. Do not refer to any external knowledge. If the answer is not in the documents, say 'Information not found'." (Useful for closed-domain Q&A).
Context-Preferred Generation: "Based on the following documents, answer the user's question. You may synthesize information from multiple documents." (Allows more fluid generation based on context).
Summarization/Extraction: "Summarize the key points from the provided documents relevant to the user's query: ..."

The clarity and specificity of these instructions directly impact the quality and faithfulness of the generated response. You are essentially programming the LLM's behavior for this specific task through the prompt.

Visualizing the Flow

The process of taking a query, retrieving context, and formatting the prompt before sending it to the LLM can be visualized as follows:

Flow diagram illustrating how a user query and retrieved context are combined by a prompt formatter to create the augmented prompt sent to the LLM.

Practical Considerations

Context Window Limits: LLMs have maximum input token limits (the context window). If you retrieve too many chunks or the chunks themselves are very large, the combined prompt might exceed this limit. Your implementation needs to handle this, perhaps by retrieving fewer chunks, truncating chunks, or using models with larger context windows. This was discussed in Chapter 3 but is particularly relevant here.
Instruction Adherence: While we instruct the LLM to use the context, models might still occasionally hallucinate or ignore the instructions. Evaluating the output (covered later) is necessary to ensure the RAG system behaves as expected.
Context Relevance: The quality of the retrieval step directly impacts this stage. If irrelevant documents are retrieved, they add noise to the prompt and can potentially degrade the quality of the final answer, even with good instructions.

By carefully structuring the prompt to include clear instructions and well-formatted context alongside the original query, you effectively provide the LLM with the specific information it needs to generate relevant, context-aware responses, overcoming the limitations of its static internal knowledge. This augmentation step is central to the effectiveness of the RAG technique.