Yash Chudasama

RAG: Building AI Systems That Know Your Data

· 5 min read

Large Language Models know a lot, but they don’t know your data. Retrieval-Augmented Generation (RAG) bridges this gap — enabling AI systems that combine the reasoning power of LLMs with the specificity of your own knowledge base. Having built several RAG-powered applications, I want to share practical insights on building systems that actually work.

What is RAG?

RAG is an architecture pattern that enhances LLM responses by retrieving relevant information from external sources before generating answers.

The basic flow:

User Query → Retrieve Relevant Documents → Augment Prompt with Context → Generate Response

Without RAG, an LLM can only answer from its training data, which is:

  • Frozen in time: No knowledge of events after training cutoff
  • Generic: No awareness of your specific documents, products, or domain
  • Hallucination-prone: May confidently generate incorrect information

With RAG, the model receives relevant context alongside the query, producing responses that are grounded in real, verifiable data.

The RAG Architecture

Step 1: Document Ingestion

Transform your data into a format suitable for retrieval:

Document Loading: Support multiple source formats — PDFs, web pages, databases, APIs, Markdown files, code repositories.

Chunking: Split documents into appropriately-sized pieces. This is more art than science:

  • Too small (50-100 tokens): Loses context and coherence
  • Too large (2000+ tokens): Dilutes relevance and wastes context window
  • Sweet spot (200-500 tokens): Balances context and specificity

Chunking strategies:

  • Fixed-size with overlap (simple, works for many cases)
  • Semantic chunking (split at natural topic boundaries)
  • Recursive splitting (respect document structure — headings, paragraphs)
  • Sentence-window (retrieve the sentence, but include surrounding context)

Step 2: Embedding and Indexing

Convert text chunks into vector embeddings — numerical representations that capture semantic meaning:

Embedding models: Models like OpenAI’s text-embedding-3-small, Cohere’s embed-v3, or open-source models like bge-large convert text into dense vectors.

Vector databases: Store and search embeddings efficiently:

  • Pinecone: Managed, scalable, production-ready
  • Weaviate: Open-source with hybrid search
  • Qdrant: High-performance, Rust-based
  • Chroma: Lightweight, developer-friendly
  • pgvector: PostgreSQL extension (great for existing Postgres users)

Step 3: Retrieval

When a user asks a question:

  1. Convert the query to an embedding using the same model
  2. Search the vector database for the most similar document chunks
  3. Return the top-k most relevant results

Retrieval strategies:

  • Semantic search: Vector similarity (cosine, dot product)
  • Keyword search: BM25 or TF-IDF for exact term matching
  • Hybrid search: Combine semantic and keyword search for best results
  • Reranking: Use a cross-encoder model to rerank initial results for higher precision

Step 4: Generation

Construct a prompt with the retrieved context and send it to the LLM:

System: You are a helpful assistant. Use the following context to answer the user's question. If the context doesn't contain the answer, say so.

Context:
[Retrieved document chunks]

User: [Original question]

Advanced RAG Patterns

Multi-Query RAG

Generate multiple variations of the user’s query to improve retrieval coverage:

Original: "How do I deploy to production?"
Variation 1: "Production deployment process and steps"
Variation 2: "Moving application from staging to production"
Variation 3: "Release management and deployment pipeline"

Retrieve documents for all variations, deduplicate, and use the combined context.

Self-RAG

The model decides whether to retrieve, what to retrieve, and whether the retrieved context is relevant:

  1. Assess if retrieval is needed for the query
  2. If yes, retrieve and evaluate relevance of each chunk
  3. Generate response using only relevant chunks
  4. Self-critique the response for groundedness

Parent-Child Retrieval

Store small chunks for precise retrieval, but return the parent document for broader context:

Parent Document (full section)
  ├── Child Chunk 1 (retrieved)
  ├── Child Chunk 2
  └── Child Chunk 3

When a child chunk matches, return the full parent document to the LLM.

Knowledge Graph RAG

Combine vector retrieval with structured knowledge graphs:

  • Extract entities and relationships from documents
  • Build a graph connecting related concepts
  • Use graph traversal alongside vector search for richer context
  • Particularly effective for complex domains with many interrelated concepts

Common Pitfalls

1. Poor Chunking

The most common RAG failure is bad chunking. Signs of poor chunking:

  • Answers lack important context
  • Retrieved chunks are irrelevant despite being semantically similar
  • The system works for simple questions but fails for complex ones

Fix: Experiment with chunk sizes, use overlap, respect document structure.

2. Insufficient Context Window

Retrieving too many chunks can overwhelm the context window, causing the model to miss relevant information.

Fix: Retrieve more than you need, then rerank and select the top results. Quality over quantity.

3. Embedding Model Mismatch

Using a general-purpose embedding model for a specialized domain can result in poor retrieval.

Fix: Consider domain-adapted embeddings or fine-tuned models for specialized use cases.

4. Ignoring Metadata

Not all relevance is semantic. A document’s date, author, category, or source can be crucial for retrieval.

Fix: Store metadata alongside embeddings and use filtered search when appropriate.

5. No Evaluation Framework

Without measuring retrieval quality, you’re flying blind.

Fix: Build evaluation datasets with query-document relevance pairs. Measure retrieval precision, recall, and end-to-end answer quality.

Evaluation Metrics

Measuring RAG performance requires evaluating both retrieval and generation:

Retrieval Metrics:

  • Precision@k: How many retrieved documents are relevant?
  • Recall@k: How many relevant documents were retrieved?
  • MRR (Mean Reciprocal Rank): How high is the first relevant result ranked?

Generation Metrics:

  • Faithfulness: Does the answer accurately reflect the retrieved context?
  • Answer Relevancy: Does the answer address the user’s question?
  • Groundedness: Is every claim in the answer supported by the context?

Tools like RAGAS, DeepEval, and LangSmith provide frameworks for automated RAG evaluation.

When to Use RAG

RAG is the right choice when:

  • You need the LLM to answer questions about specific, private data
  • Your data changes frequently (RAG updates are simpler than fine-tuning)
  • You need verifiable, source-attributed answers
  • You want to avoid the cost and complexity of model fine-tuning

RAG is not the best choice when:

  • You need the model to learn a new skill or behavior (use fine-tuning)
  • Your queries don’t require external knowledge
  • Real-time latency requirements are extremely tight (<100ms)

Conclusion

RAG has become the standard pattern for building AI applications that work with custom data. The architecture is straightforward, but the details matter — chunking strategy, embedding model selection, retrieval approach, and evaluation all significantly impact quality.

The key insight is that RAG is not just a technical implementation — it’s a design philosophy: ground AI responses in real data, make them verifiable, and give users confidence that the answers they receive are based on facts, not fabrication.

As AI continues to integrate into enterprise applications, RAG will be the foundation that makes LLMs practical for real-world use cases.