CAG vs. RAG in N8N: Choosing the Right Retrieval Technique for Your AI Workflows

Large Language Model (LLM) context windows have exploded recently, paving the way for techniques like Cache-Augmented Generation (CAG). But does this newcomer replace the established Retrieval-Augmented Generation (RAG)? This article dives deep into both methods, exploring their mechanics, pros, cons, and implementation nuances within N8N using popular models like Gemini, OpenAI, and Anthropic (Claude). If you're building AI-powered applications in N8N and want to understand which retrieval strategy best suits your needs—balancing accuracy, speed, cost, and complexity—this guide is for you.

Introduction: The Rise of CAG Amidst Larger Context Windows

Over the last six months, we've witnessed a dramatic increase in the context window length offered by leading Large Language Models (LLMs). This expansion has fueled interest in Cache-Augmented Generation (CAG), a retrieval technique that leverages these larger windows. But how does it stack up against the tried-and-tested Retrieval-Augmented Generation (RAG), especially within an automation platform like N8N?

This article explores how you can implement both CAG and RAG in N8N, comparing their effectiveness with OpenAI, Anthropic (Claude), and Google Gemini models to help you decide which approach fits your specific use case.

Understanding RAG (Retrieval-Augmented Generation)

RAG has become a standard architecture for building knowledge-based AI applications. It typically involves two main stages:

1. Ingestion/Import:

   • Documents (e.g., PDFs from Google Drive) are loaded.
   • They are broken down into smaller chunks based on defined size and overlap rules.
   • Each chunk is converted into a numerical vector representation using an embedding model.
   • These vectors are stored and indexed in a vector database (like Quadrant, Pinecone, or Supabase).

   Note: This stage requires ongoing maintenance. Document changes necessitate updating or deleting corresponding vectors in the database.

2. Querying/Retrieval:

   • A user submits a query (e.g., a chat message).
   • The query is also embedded into a vector.
   • The vector database is searched for document chunks with vectors most similar (numerically closest) to the query vector.
   • The top K (e.g., 10 or 30) most similar chunks are retrieved.
   • The original query and the retrieved chunks are sent to the LLM.
   • The LLM generates a response based on the query and the provided context (chunks).

RAG Limitations

While powerful, RAG isn't flawless:

• Limited Context: Providing only the top K chunks means the LLM sees only a fraction of the original document(s). As demonstrated with an F1 technical manual example, using only 10 chunks might yield a less comprehensive answer than using 30, or potentially less than a CAG approach using the entire document.
• Irrelevant Chunks: Vector similarity doesn't always guarantee contextual relevance. Sometimes, mathematically similar chunks might not be helpful for answering the specific query, especially with diverse document sets.
• Contextual Gaps: Chunking can split related information. A paragraph might mention "Berlin," but sentences later in the same chunk might lack the keyword, potentially causing them to rank lower for a "Berlin" query, even if relevant.

Techniques like contextual retrieval and reranking exist to mitigate these issues, but they add complexity.

Exploring CAG (Cache-Augmented Generation)

CAG offers a different approach, leveraging the LLM's ability to process larger amounts of text directly, often using built-in caching mechanisms. There are primarily two flavors:

1. OpenAI & Anthropic Version (Prompt Caching)

This method relies on the LLM's internal, short-term caching:

• Process: When a query is made, the entire relevant document(s) are sent along with the query to the LLM (OpenAI or Anthropic). The LLM generates the output.
• Caching: For follow-up questions within a short timeframe, you resend the entire document(s) and the new query. The LLM provider (server-side) recognizes the identical document prefix from the recent request and utilizes its internal cache (like a KV cache) for that portion. This reduces processing load and potentially cost for the cached part, leading to faster responses on subsequent calls.
• Setup: Relatively simple. However, for OpenAI, prompt structure is crucial for effective caching – static content (like the document) must precede dynamic content (the query). For Anthropic, you often need to use an HTTP Request node in N8N to pass a specific cache_control parameter.
• Example (OpenAI): A first request with a document might use ~5,000 tokens. A follow-up request (sending the document again) might process much faster, and the response details could show thousands of cached_tokens, confirming the cache was used.
• Example (Anthropic): Similarly, sending the document again with the cache_control flag results in response details showing significant cache_read_input_tokens, confirming cache usage.
• Caveats:
  • Cache TTL: The cache duration is typically short (5-10 minutes, maybe up to an hour).
  • Rate Limits: Repeatedly sending large documents can hit API rate limits, as sometimes observed with Anthropic, potentially requiring plan upgrades.

2. Google Gemini Version (Explicit Caching)

Google Gemini's approach offers more control but requires more setup:

• Upload Step: You explicitly upload the document content to Gemini's cache via a dedicated API endpoint. This returns a unique cache ID.
• Storage: The user is responsible for storing this cache ID (e.g., in a database like NoCodeDB, Airtable, or SQL DB) and managing its lifecycle, including its Time To Live (TTL).
• Query Step: When submitting a query, you send only the query and the corresponding cache ID to the Gemini generation endpoint.
• Server-Side Augmentation: Gemini uses the cache ID to retrieve the full document content from its cache server-side, combining it with your query to generate the response.
• Benefits:
  • Avoids resending large documents with each query.
  • Offers much longer cache durations (configurable TTL, potentially hours or days).
  • Can associate system_instructions directly with the cache.
• Drawbacks:
  • Significantly more complex setup involving separate ingestion workflows, cache ID database management, and TTL tracking/refresh logic.
  • Requires careful handling of cache expirations.

Gemini CAG Workflow Example (Conceptual):

1. Ingestion: An N8N workflow monitors for new files, extracts text, checks token count (Gemini often requires a minimum, e.g., >32k tokens), encodes content (Base64), uploads to Gemini's cachedContents endpoint with a specified TTL, receives a cache ID, and saves the ID and expiry time to your database.
2. Querying: Another N8N workflow receives a user query, fetches the valid (non-expired) cache ID from your database, and sends the query plus the cache ID to the Gemini generation endpoint for a fast response.

Head-to-Head Comparison: RAG vs. CAG

Let's compare these two techniques across key dimensions:

This comparison highlights that the 'better' approach is highly dependent on the specific requirements of your application.

When to Use RAG vs. CAG?

Based on the comparison, here’s a guideline:

• Choose RAG when:

  • You need to query across a large, diverse set of documents (thousands or millions).
  • Data freshness is critical, and information changes frequently.
  • You need fine-grained control over the retrieval process (chunking strategy, reranking).
  • Per-query LLM cost needs to be minimized (by sending smaller contexts).

• Choose CAG when:

  • You are working with a smaller, relatively static knowledge base (e.g., one or a few large documents like manuals, books, reports) that fits within the LLM's context/cache limits.
  • Low latency / high speed responses are paramount.
  • You want to leverage the LLM's ability to reason over the entire document context at once, potentially improving answer comprehensiveness for certain queries.
  • You want to potentially simplify the initial setup by avoiding the vector database pipeline (especially with OpenAI/Anthropic versions).

Conclusion: Complementary Tools, Not Replacements

Neither CAG nor RAG is definitively superior; they are complementary techniques, each excelling in different scenarios. RAG remains the dominant architecture for scalable, dynamic knowledge retrieval.

However, as LLM context windows continue to grow, inference costs potentially decrease, and provider caching mechanisms improve (offering better performance and discounts), CAG is likely to become increasingly viable and appealing. It offers a compelling alternative, particularly for applications prioritizing speed and full-document context over massive scale or data volatility, potentially bypassing the complexities of managing a full RAG pipeline.

The choice between RAG and CAG depends on a careful evaluation of your project's specific needs regarding knowledge base size, data dynamics, performance requirements, and acceptable complexity and cost.