Why AI Apps Break & How to Fix Them: A Context Engineering Guide

TL;DR

The common issue of Retrieval-Augmented Generation (RAG) pipelines failing to utilize retrieved context stems from poor prompt structure. The core solution is Context Pre-Attending, an engineering framework that forces the Language Model (LLM) to parse and internalize the context before processing the user's query. This significantly reduces hallucinations and improves factual grounding, leading to better AI app reliability.

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The Problem: Context Blindness in RAG-Powered AI Apps

When building a RAG pipeline, the most persistent technical challenge is context blindness. Your retriever successfully fetches the most relevant documents, but the LLM, despite having the context prepended to the prompt, frequently ignores it, fabricates an answer (hallucinates), or defaults to its internal knowledge. This happens because the model often treats the unstructured, large block of retrieved text as lower-priority data peripheral to the core user question. This is a common cause of unreliable AI agent behavior. This makes debugging AI agents a nightmare, as the root cause is hidden within the model's opaque reasoning process.

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The Solution: Context Pre-Attending (CPA)

We can solve this using a technique called Context Pre-Attending (CPA). Instead of simply dumping the retrieved text into the prompt, CPA formats the context into a structured, highly directive preamble that the LLM must first acknowledge and process. This framework represents a shift in AI architecture, moving from passive context provision to active context enforcement. It consists of three parts:

1. Directive Header: A strict instruction to only use the provided context.
2. Structured Context Block: The retrieved data, formatted with clear delimiters and labeled sources.
3. Mandatory Reflection/Constraint: A final command that frames the user's query and imposes a strict output constraint (e.g., "If the answer is not in the context, state 'Not found in provided documents.'").

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Implementing CPA: A Step-by-Step Guide

This tutorial assumes you have a basic RAG setup and are focusing on the final prompt construction. We'll use Python for the implementation.

Step 1: Define the Structured Context Block

First, we must format the retrieved documents into a parseable structure. Use consistent, unique delimiters and explicitly label each source.

# Python snippet
retrieved_documents = [
    {"source": "Doc_A.pdf", "text": "The project's primary goal is to minimize latency."},
    {"source": "Manual_V2.0", "text": "Latency optimization requires a re-indexing strategy."}
]

def format_context_block(documents):
    """Formats retrieved docs into a structured, delimited string."""
    context_parts = []
    for i, doc in enumerate(documents):
        block = (
            f"\n***DOCUMENT START {i+1} - Source: {doc['source']}***\n"
            f"{doc['text']}"
            f"\n***DOCUMENT END {i+1}***\n"
        )
        context_parts.append(block)
    
    return "\n".join(context_parts)

structured_context = format_context_block(retrieved_documents)

Step 2: Construct the CPA System Prompt

The complete prompt integrates the three CPA components: Header, Context Block, and Constraint.

# Python snippet
def construct_cpa_prompt(structured_context, user_query):
    """Assembles the full CPA-compliant prompt."""
    
    # 1. Directive Header
    header = (
        "You are an expert Q&A assistant. Your response **MUST** be based "
        "**EXCLUSIVELY** on the following provided documents. Do not use any "
        "external knowledge.\n\n"
    )
    
    # 2. Structured Context Block
    context_block = f"--- CONTEXT DOCUMENTS START ---\n{structured_context}\n--- CONTEXT DOCUMENTS END ---\n\n"
    
    # 3. Mandatory Reflection/Constraint & User Query Frame
    constraint_and_query = (
        "Carefully analyze the documents above. Your final answer must be a "
        "direct response to the User Query below. **If the answer cannot be "
        "found in the provided context, you MUST reply only with: 'ERROR: Contextual information not available.'**\n\n"
        f"--- USER QUERY ---\n{user_query}\n"
        f"--- ANSWER ---\n"
    )
    
    return header + context_block + constraint_and_query

user_query = "What is the primary goal of the project, and what strategy is needed for optimization?"
final_prompt = construct_cpa_prompt(structured_context, user_query)

Step 3: Implement the LLM API Call

Pass the final_prompt directly to your chosen LLM (e.g., OpenAI, Anthropic, Gemini).

# Python snippet (Conceptual - using a placeholder LLM function)
def call_llm_with_cpa(system_prompt):
    """Placeholder for the LLM API call."""
    # In a real application, you would call the LLM API here.
    # For more details on RAG, see the original paper: https://arxiv.org/abs/2005.11401
    return "The project's primary goal is to minimize latency. Latency optimization requires a re-indexing strategy."

llm_response = call_llm_with_cpa(final_prompt)
print(f"\nLLM Response:\n{llm_response}")

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Verification & Testing Your AI App

The CPA framework is verified by running two specific test cases:

1. Context-Exclusive Test (Success Case):
   • Query: Ask a question whose answer exists only in the retrieved context.
   • Expected Result: The LLM uses the exact data from the context.
2. Context-Missing Test (Failure/Constraint Case):
   • Query: Ask a question whose answer is not present in the retrieved context.
   • Expected Result: The LLM returns the strictly defined constraint response: 'ERROR: Contextual information not available.'

Success in the second test case is the strongest confirmation that the CPA framework has successfully overridden the model's internal knowledge.

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Key Considerations & Trade-offs

• Token Consumption: CPA increases the system prompt length. This can increase latency and cost.
• Context Volume: If you retrieve a very large volume of documents, the LLM's attention may still struggle. For these cases, consider a Context Summarization Layer to pre-process the context.
• Delimiters: The choice of unique delimiters is critical. Test multiple options to find one that the specific LLM you are using consistently recognizes as a structural break.

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Need Help Fixing Context Blindness?

Struggling with RAG pipelines or hallucinations in your AI apps? Request a free context audit and make your AI app reliable.