On this page
The Core Concept: Decoupling Knowledge from Intelligence
Phase 1: The Data Ingestion Pipeline
Chunking Strategies
Embeddings and Vectorization
Phase 2: The Retrieval Layer
The Vector Database
Indexing for Scale
Phase 3: Optimizing for Accuracy
Hybrid Search
Re-ranking Services
Phase 4: The Generation Phase
Context Construction
Reducing Hallucinations
Scaling Considerations
Sharding
Caching
Conclusion
System Design for RAG (Retrieval-Augmented Generation): Vector Databases, Chunking, and Re-ranking
Large Language Models (LLMs) have fundamentally changed how software processes information. However, these models have significant limitations regarding their internal knowledge.
An LLM is restricted to the data present during its training phase. It does not possess awareness of current events, private organizational data, or information released after its training cutoff.
When a model attempts to answer questions about data it has never processed, it often produces confident but factually incorrect answers. In the industry, we refer to these as hallucinations.
Retrieval-Augmented Generation (RAG) is the standard architectural pattern used to resolve this limitation. It allows developers to connect a static LLM to a dynamic, external knowledge source.
By retrieving relevant data and feeding it to the model alongside the user's question, the system grounds the AI in reality.
For a junior developer or a candidate preparing for a System Design Interview, understanding RAG is no longer optional. It is a core component of modern distributed systems.
This guide will walk you through the architecture of a RAG system. We will focus on how to maintain accuracy while scaling to millions of documents.
The Core Concept: Decoupling Knowledge from Intelligence
Retrieval-Augmented Generation is a process that separates knowledge from intelligence. The LLM provides the intelligence (reasoning and language generation), while an external database provides the knowledge.
The workflow consists of three main phases:
- Ingestion: Preparing the data so it can be searched efficiently.
- Retrieval: Identifying the specific pieces of data relevant to a user's query.
- Generation: Using the retrieved data to synthesize an accurate response.
While the concept appears straightforward, the complexity lies in the implementation details. You must make critical design decisions about how to split text, how to represent meaning mathematically, and how to search through vast datasets in milliseconds.
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Phase 1: The Data Ingestion Pipeline
Before a system can answer questions, it must process the source data. This is an asynchronous process that occurs before any user interacts with the system.
The goal of this pipeline is to transform raw unstructured text into a format optimized for machine understanding.
Chunking Strategies
You cannot simply feed an entire 50-page PDF into an LLM. Models have a context window, which is a strict limit on the amount of text they can process in a single request.
Furthermore, searching for a whole document is rarely useful. The goal is to find the specific paragraph that contains the answer.
To address this, we use chunking. This is the process of breaking large documents into smaller, manageable segments of text.
Choosing a chunking strategy is a critical design decision.
Fixed-size chunking is the most basic approach. You split the text every 500 or 1,000 characters. This is computationally cheap and easy to implement.
However, it poses a risk to data integrity. A strict character cut might sever a sentence in the middle or separate a subject from its verb. This destroys the semantic meaning of that segment.
Semantic chunking is a superior strategy for accuracy. This method splits text based on natural breaks, such as new paragraphs, section headers, or changes in topic. This ensures that each chunk represents a complete, self-contained idea.
To further prevent data loss, we implement chunk overlap.
If the chunk size is 500 tokens, we might set an overlap of 50 tokens. This means the last 50 tokens of chunk A are repeated as the first 50 tokens of chunk B.
This ensures that if an important concept sits on the boundary of two chunks, it is fully captured in at least one of them.
Embeddings and Vectorization
Once data is chunked, the computer needs a way to understand the meaning of the text.
Computers do not process words; they process numbers. This is where vector embeddings are applied.
An embedding model is a specialized AI model that converts text into a long list of numbers, known as a vector.
This vector represents the semantic meaning of the text in a high-dimensional space.
For instance, the words "canine" and "dog" look completely different in text. However, a robust embedding model will produce vectors for these words that are mathematically very close to each other.
Conversely, "dog" and "banana" will have vectors that are far apart.
In a RAG system, every single chunk of data is passed through an embedding model. The resulting vectors are what the system stores.
This adds embedding latency to the ingestion pipeline.
If you are processing millions of documents, the time it takes to generate these vectors becomes a significant bottleneck. You typically handle this by parallelizing the work using a distributed task queue.
Phase 2: The Retrieval Layer
When a user asks a question, the system needs to find the most relevant chunks. This is the "Retrieval" component of RAG.
The Vector Database
Traditional databases (like SQL) are designed for exact matches.
If you search for "server error," they look for those exact strings.
In RAG, we require semantic search. We need to find text that means the same thing as the query, even if the phrasing is different.
To accomplish this, we use a Vector Database. This is a specialized storage engine optimized for storing and searching high-dimensional vectors.
When a user submits a query:
- The application converts the user's text query into a vector using the same embedding model used during ingestion.
- The vector database compares the query vector against all stored document vectors.
- It calculates a similarity score (often using Cosine Similarity) to determine how close the vectors are.
Indexing for Scale
Calculating the distance between the query vector and every single document vector is computationally expensive. This is known as a "flat search" or "brute force search." It provides perfect accuracy but is too slow for large datasets.
To solve this, vector databases use Approximate Nearest Neighbor (ANN) algorithms.
A widely adopted algorithm is HNSW (Hierarchical Navigable Small World).
HNSW builds a graph structure that connects vectors based on their proximity. It creates multiple layers of navigation.
The top layers allow the search algorithm to jump across the dataset quickly to find the general neighborhood of the answer. The lower layers allow for precise traversing to find the exact closest matches.
This approach drastically reduces search time from seconds to milliseconds.
The trade-off is a slight potential loss in accuracy, but for most RAG applications, the speed gain is necessary.
Phase 3: Optimizing for Accuracy
A standard vector search is fast, but it is not always precise. It might retrieve chunks that are topically related but do not contain the specific answer.
To improve the quality of the results, we introduce advanced retrieval techniques.
Hybrid Search
Vector search excels at understanding concepts, but it sometimes struggles with specific keywords (like unique error codes, part numbers, or acronyms).
Keyword search (often using the BM25 algorithm) is excellent at exact matching but fails at understanding context.
Hybrid Search combines both methodologies.
The system runs a vector search and a keyword search in parallel. It then fuses the results using a mathematical formula, such as Reciprocal Rank Fusion (RRF). This provides the system with the best of both worlds: the conceptual understanding of vectors and the precision of keyword matching.
Re-ranking Services
Even with hybrid search, the top results might not be ranked perfectly.
The vector database is optimized for retrieval speed, not deep comprehension. It uses a "Bi-Encoder" approach, where the query and document are processed separately.
To fix this, we add a Re-ranking step.
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The vector database retrieves a larger set of candidates (e.g., the top 50 chunks).
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These 50 chunks are passed to a Cross-Encoder model (the Re-ranker).
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The Cross-Encoder is a more powerful, slower model that analyzes the query and the document pair together. It scores them based on how well the document specifically answers the question.
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The system selects the top 5 (out of the 50) re-ranked chunks to send to the LLM.
Re-ranking significantly increases accuracy but adds latency. It is a classic system design trade-off.
You sacrifice a few hundred milliseconds of processing time to ensure the user receives a correct answer rather than an irrelevant one.
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Phase 4: The Generation Phase
Once the relevant chunks are retrieved and ranked, the final step is generating the answer.
Context Construction
The system constructs a text prompt that typically follows this structure:
- System Instruction: "You are a helpful assistant. Use only the provided context to answer the question."
- Context: [Insert the text from the top 5 retrieved chunks here].
- User Question: [Insert user query here].
This aggregate text is sent to the LLM.
The context window limit is crucial here. If you retrieve too many chunks, or if your chunks are too large, the prompt will exceed the model's limit.
This causes the request to fail or forces the system to truncate valuable information.
Reducing Hallucinations
By explicitly instructing the model to "use only the provided context," you reduce the chance of hallucination. However, it is not a guarantee. If the retrieved chunks do not contain the answer, the model might attempt to guess.
A robust design includes instructions for the model to reply "I do not know" if the context is insufficient. This ensures reliability over creativity, which is essential for enterprise applications.
Scaling Considerations
As your dataset grows from thousands to hundreds of millions of vectors, a single server cannot hold the entire index in memory. You must design for scale.
Sharding
Sharding involves splitting the vector index across multiple machines.
- Horizontal Scaling: You add more nodes to the cluster.
- The dataset is partitioned. When a query arrives, it is distributed to all shards in parallel.
- Each shard searches its portion of the data and returns its top results.
- A central aggregator combines the results and returns the final list.
Sharding increases infrastructure complexity but allows the system to handle virtually unlimited data volume.
Caching
To reduce cost and latency, you should implement caching at multiple levels.
- Semantic Caching: If a user asks a question that is semantically identical to a previous question (e.g., "How do I reset my password?" vs. "Password reset instructions"), the system can detect this similarity. It can then return the previous LLM response immediately. This bypasses the entire retrieval and generation chain, providing an instant response.
Conclusion
Building a RAG system requires orchestrating a pipeline that balances accuracy, latency, and cost. It requires a deep understanding of how data is processed, stored, and retrieved.
Here are the key takeaways for your system design strategy:
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Data Preparation is Critical: Your retrieval quality depends heavily on your chunking strategy. Use semantic chunking to preserve meaning.
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Vector Databases are Essential: Use them for semantic search, but understand the trade-offs between exact search and approximate (ANN) search.
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Latency Matters: Embedding generation and vector search take time. Use in-memory indexes and parallel processing to keep the system responsive.
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Accuracy Requires Layers: Vector search alone is often insufficient. Implement Hybrid Search and Re-ranking to ensure the most relevant data reaches the LLM.
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Scale Horizontally: Plan for sharding early if you expect your dataset to grow beyond the memory capacity of a single machine.
By mastering these components, you move beyond simple API integrations and start engineering robust, production-grade AI architectures.
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