Playbook
We will provide a playbook of building a RAG system and walk you through each activity and through each important layer.
RAG system has a multi-layered architecture that is built through various activities and tasks and lives across multiple layers. Activities consist of data processing, knowledge management and intelligent reasoning whilst there are various database and orchestration layers involved. It encompasses everything from ingesting raw data to delivering answers through a user interface. The goal is to organize and orchestrate all components, from data ingestion, knowledge bases, models, memory, tools, and guardrails in order for them to work together and produce accurate, context-aware and safe outputs.
Below is a high level overview:
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Data Ingestion and Preprocessing: Intake of data from various sources, converting files and records into usable text. This stage cleans, enriches, and splits data into manageable chunks for downstream processing.
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Embedding and Knowledge Graph Generation: Construction of knowledge representations. Transformation of your data and documents into vector or graph domains. This may involve creating a vector embedding index of documents for semantic search, building a knowledge graph of entities and relationships or a hybrid of both for comprehensive retrieval.
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Data Storage: Storage solutions for your knowledge bases. Typically includes a Vector Database (for similarity search on embeddings) and/or a Graph Database (for querying structured relationships) to serve as the system’s long-term memory.
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Language Model: The AI brain of the stack. The generator. A Large Language Model (LLM) is often used for understanding queries and generating answers, but is sometimes complemented by a Small Language Model (SLM) for specialized or efficient domain specific sub-tasks.
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RAG Orchestration: The control logic (Retrieval-Augmented Generation orchestration) that coordinates data retrieval and generation. It ensures relevant information is fetched from knowledge bases and supplied to the LLM to ground its responses.
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Tool and Action Interfaces: Mechanisms that allow the agent to perform actions beyond just text generation. Tools (APIs, calculators, web search and others) can be invoked by the model (via function calls or an agent framework) to extend capabilities.
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Agent Memory and State Management: Components that track conversation history and agent state through the actions and decisions. This includes handling what the user has said before, the agent’s own chain-of-thought, and any working notes so the agent can maintain context and learn over time. This is effectively system's short term memory.
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Execution Controller and Task Scheduler: The system that breaks down complex user requests into tasks and sequences them. It manages multi-step workflows. It decides what to do first and what to do next and can schedule or parallelize actions as needed for complex tasks.
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Guardrails and Policy Enforcement: A layer of rules and filters to ensure that agent’s behavior is safe and aligned with company policies or personal desires. It checks inputs and outputs for issues like harmful content, privacy violations or forbidden topics and then enforces proper formats and disclaimers.
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Additional Memory Layers: Advanced memory mechanisms beyond basic conversation context. This can include long-term memory stores (for facts learned across sessions), summary memory (to compress older dialogues) or multi-modal memory where each helping the agent retains important information over time.
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UI Front End: The user interface through which users interact with the agent. A well-designed front end presents the conversation, allows follow-up questions, displays sources or citations for transparency, and handles user feedback.
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Infrastructure and DevOps: The underlying deployment and operations framework. This covers containerization, cloud services, databases, and continuous integration/monitoring that keep the whole system running reliably and efficiently in production.
In the sections that follow, we will dive into each part in detail. By understanding each activity and layer and how its interplays with others, you will have a good grasp on the steps needed to actually build RAG systems and transform raw data into useful AI-driven interactions.