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Understanding RAG

RAG is a powerful technique that enhances an LLM’s knowledge by connecting it to an external data source at inference time.9 Instead of relying solely on the static, pre-trained information baked into its parameters, the model is given access to a dynamic, up-to-date knowledge base. The typical RAG workflow involves several steps 10:
  1. Indexing: A corpus of documents (e.g., company policies, technical manuals, support articles) is processed. The documents are broken down into smaller, manageable chunks. Each chunk is then passed through an embedding model to create a numerical vector representation, which captures its semantic meaning. These vectors are stored in a specialized vector database (e.g., Pinecone, Milvus, Weaviate) for efficient searching.10
  2. Retrieval: When a user submits a query, the query itself is also converted into a vector embedding. The system then performs a similarity search in the vector database to find the document chunks whose embeddings are most semantically similar to the query’s embedding.7
  3. Augmentation: The top-ranked, most relevant document chunks are retrieved and “augmented” into the LLM’s context window alongside the original user query.
  4. Generation: The LLM then generates a response, drawing upon both its internal knowledge and the specific, relevant context provided by the retrieved documents.
The benefits of this approach are profound. It ensures responses are factually grounded, reduces the likelihood of making things up (hallucination), allows the AI’s knowledge to be updated simply by updating the document store, and provides auditability by allowing the system to cite its sources.8

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RAG as an Implementation of CWA Layer 3

The relationship between RAG and CWA is direct and complementary: RAG is the primary implementation pattern for CWA’s Layer 3: Curated Knowledge Context. The entire RAG process—retrieving relevant information from a knowledge base to ground the LLM—is precisely the function that Layer 3 is designed to fulfill within the broader architecture.1 CWA does not seek to replace RAG; it contextualizes it. It recognizes that providing curated knowledge is a critical architectural concern and dedicates a specific layer to it. The CWA model then shows how to make that RAG-retrieved context even more powerful by surrounding it with other essential information. For example, the query used for the retrieval step (in the RAG workflow) can be enriched with information from Layer 2 (User Info) to fetch more personalized results. The final generated answer can be constrained by Layer 10 (Dynamic Output Formatting) to ensure it’s usable.

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The Limits of RAG-Only Systems

This relationship also highlights the limitations of systems built only around RAG. While powerful, a RAG-only architecture is incomplete for many sophisticated enterprise use cases. Such systems often lack:
  • Personalization (Layer 2): A standard RAG system treats all users the same. It cannot tailor its retrieved information or its final response based on the user’s role, preferences, or history.
  • Task Management (Layer 4): A simple RAG system is stateless. It is designed for single-shot question-answering and cannot manage a multi-step task, track progress, or guide a user through a complex workflow.
  • Sophisticated Tool Use (Layers 7 & 8): RAG is typically focused on retrieving information from a static document store. It does not inherently provide a mechanism for the AI to interact with APIs, execute code, or perform actions in external systems.
By viewing RAG through the lens of CWA, it becomes clear that it is one crucial component among many. A truly robust AI application requires not just retrieved knowledge, but also personalization, state management, and the ability to act.

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Advanced RAG Patterns and CWA

The field of RAG is itself evolving beyond simple vector search. Several advanced RAG patterns have emerged, and CWA provides a natural architectural home for orchestrating them.
  • Structured RAG: This pattern involves retrieving information from structured data sources like SQL databases or knowledge graphs (GraphRAG).14 This often requires the LLM to first generate a query (e.g., a SQL statement) and then execute it. This pattern maps perfectly to a combination of CWA layers. The description of the database schema or graph ontology resides in
    Layer 7 (Tool Explanation), the LLM’s action of generating and running the query is an instance of tool use, and the data returned by the database populates Layer 8 (Function Call Results), which is then used alongside Layer 3 (Curated Knowledge Context) to generate the final answer.
  • API-Augmented RAG: This pattern retrieves real-time, dynamic information by calling external APIs.14 For example, an AI might call a weather API or a stock market data API. This is a direct implementation of
    CWA Layers 7 and 8. The API’s specification is the “Tool Explanation,” and the live data it returns is the “Function Call Result.” This demonstrates that the concept of “retrieval” in CWA is broader than just static documents.
  • Self-Corrective / Iterative RAG: Advanced RAG systems can refine their own retrieval process. They might decompose a complex question into sub-questions, reflect on the quality of retrieved documents and re-query if they are irrelevant, or re-rank results for better coherence.10 This iterative reasoning process can be guided and managed by the structured objectives and sub-task tracking defined in CWA Layer 4 (Task/Goal State Context).

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Alternative RAG Architectures and their CWA Layer Mapping

This table visually demonstrates how CWA serves as a superset architecture that accommodates and orchestrates various RAG patterns, refuting any misconception that they are competing ideas.
RAG PatternDescriptionPrimary CWA Layer(s) Implemented
Vector-Based RAGRetrieves semantically similar text chunks from an unstructured document corpus stored in a vector database. The most common form of RAG. 14Layer 3 (Curated Knowledge Context): Directly populates this layer with retrieved text.
Structured RAGRetrieves data by generating and executing queries against a structured database (e.g., SQL) or knowledge graph (e.g., Cypher). 14Layer 7 (Tool Explanation): Contains the database schema. Layer 8 (Function Call Results): Contains the query results. Layer 3 (Curated Knowledge Context): The results are used as grounding knowledge.
API-Augmented RAGRetrieves real-time data by calling external APIs (e.g., for weather, stock prices, or flight information). 14Layer 7 (Tool Explanation): Contains the API specifications. Layer 8 (Function Call Results): Contains the live data returned from the API call.
Knowledge-Based RAGRetrieves information from structured knowledge representations like ontologies or rule-based systems, enabling more precise and explainable reasoning. 14Layer 7 (Tool Explanation): Describes the knowledge base rules/ontology. Layer 8 (Function Call Results): Contains the output of the rule engine or knowledge graph traversal.
Self-Corrective RAGInvolves an iterative process where the agent refines its query or evaluates the relevance of retrieved documents to improve the final result. 10Layer 4 (Task/Goal State Context): Manages the iterative process, tracking the goal and the status of sub-queries. Layer 3 (Curated Knowledge Context): Is refined over multiple steps.