Context Injection Methods

Context injection involves integrating relevant text passages, or 'context', into the prompt that will be sent to a Large Language Model (LLM). The way this context is injected significantly influences the LLM's ability to utilize it effectively. Common methods for context injection are discussed here.

Direct Concatenation

The most straightforward approach is simply prepending or appending the retrieved context directly to the original user query. Often, a separator or introductory phrase is used.

Example Structure:

Context:
[Retrieved Passage 1]
[Retrieved Passage 2]
...

Based on the context above, answer the following question: [User Query]

Alternatively, the query might come first:

Question: [User Query]

Use the following information to answer the question:
[Retrieved Passage 1]
[Retrieved Passage 2]
...

• Advantages: Simple to implement. Requires minimal prompt engineering.
• Disadvantages: Can be sensitive to the order and formatting. If many passages are retrieved, the original query might get "lost" in the noise, or important context might appear too far from the query for the LLM to focus on effectively. It also makes it harder to control how the LLM uses the context versus its internal knowledge.

Templated Injection

A more structured and generally preferred method involves using prompt templates. These are pre-defined strings with placeholders for the query and the context. Python's f-strings or dedicated templating libraries (like Jinja2, often used within frameworks like LangChain) make this manageable.

Example Template (Python f-string):

# Assume 'retrieved_docs' is a list of strings
# and 'user_query' is the original question
context_string = "\n".join(retrieved_docs)

prompt_template = f"""
You are an assistant tasked with answering questions based on the provided context.
Do not use any information outside of the context below.

Context:
{context_string}

Question: {user_query}

Answer:
"""

# 'prompt_template' now holds the fully formed prompt for the LLM

• Advantages: Offers greater control over the prompt structure. Allows clear instructions to the LLM on how to use the context (e.g., "Answer based only on the provided context"). Makes prompts easier to manage, version, and debug.
• Disadvantages: Requires slightly more setup than direct concatenation. Designing effective templates can involve some experimentation.

Structured Input Formats

Some LLMs or interaction frameworks might support more structured input formats, potentially accepting the query and context as separate parameters or fields within an object.

Example (API call):

response = llm_api.generate(
  query="What is RAG?",
  context_documents=[
    "RAG stands for Retrieve-Augmented Generation...",
    "It combines retrieval with generation..."
  ],
  instructions="Answer the query using only the provided documents."
)

• Advantages: Can be very clean and explicit. Might allow the underlying model architecture to handle context segregation more effectively.
• Disadvantages: Less common for standard open-source or widely available commercial LLM APIs, which typically expect a single string prompt. Support varies significantly between models and platforms.

Placement within the Prompt

Where you place the context within the template also matters. Common patterns include:

1. Context First: Place all context before the query and instructions.
2. Query First: Place the query first, followed by instructions and then context.
3. Interleaved (Advanced): In more complex scenarios, context might be interleaved with instructions or parts of a reasoning process. This is less common in basic RAG.

The optimal placement can depend on the specific LLM being used and the nature of the task. Some models exhibit a recency bias, paying more attention to information appearing later in the prompt. Experimentation is often necessary.

The diagram below illustrates the flow for templated injection:

A user query and retrieved context passages are inserted into designated placeholders within a prompt template. The resulting formatted prompt is then sent to the LLM.

Choosing the right injection method involves balancing implementation simplicity with the need for control over the LLM's behavior and optimizing how it uses the provided information. Templating offers a good combination of flexibility and control for most RAG applications. As you build your RAG system, consider how these different injection strategies might affect the final generated output, especially when dealing with varying amounts of retrieved context.