RAG (Retrieval-Augmented Generation): Build Smarter AI Applications
Learn how to build RAG applications that combine LLMs with your own data. Explore LangChain, LlamaIndex, and vector databases for knowledge-grounded AI.
Written by Alexandre Le Corre
•3 min read
Retrieval-Augmented Generation (RAG) has become the standard approach for building AI applications that need to access custom knowledge. Instead of relying solely on what an LLM learned during training, RAG lets you augment responses with relevant information from your own documents.
Why RAG Matters
LLMs have limitations:
- Knowledge cutoff dates
- No access to private data
- Tendency to hallucinate
- Generic responses
RAG solves these by retrieving relevant context before generation, resulting in:
- Up-to-date information
- Access to proprietary knowledge
- Grounded, verifiable responses
- Domain-specific answers
How RAG Works
┌─────────────────────────────────────────────────┐
│ RAG Pipeline │
├─────────────────────────────────────────────────┤
│ 1. Document Ingestion │
│ └── Split documents into chunks │
│ └── Generate embeddings │
│ └── Store in vector database │
├─────────────────────────────────────────────────┤
│ 2. Query Processing │
│ └── Embed user question │
│ └── Search for similar chunks │
│ └── Retrieve top-k results │
├─────────────────────────────────────────────────┤
│ 3. Generation │
│ └── Combine question + retrieved context │
│ └── Send to LLM │
│ └── Generate grounded response │
└─────────────────────────────────────────────────┘
Essential RAG Tools
LlamaIndex
Strengths:
- Data connectors for 160+ sources
- Advanced indexing strategies
- Query optimization
- Excellent documentation
LangChain
Strengths:
- Modular architecture
- Extensive integrations
- Agent compatibility
- Large community
Haystack
Strengths:
- Production-focused
- Hybrid search
- Pipeline architecture
- Enterprise features
Vector Databases for RAG
Your choice of vector database significantly impacts RAG performance:
Building a Basic RAG System
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# 1. Load documents
documents = SimpleDirectoryReader("./data").load_data()
# 2. Create index (embeddings + storage)
index = VectorStoreIndex.from_documents(documents)
# 3. Query
query_engine = index.as_query_engine()
response = query_engine.query("What is our return policy?")
print(response)
That's it – a complete RAG system in 10 lines.
Advanced RAG Techniques
Chunking Strategies
How you split documents matters:
- Fixed size: Simple but may break context
- Semantic: Split at natural boundaries
- Recursive: Hierarchical splitting
- Document-aware: Respect structure (headers, paragraphs)
Retrieval Optimization
Improve what gets retrieved:
- Hybrid search: Combine vector + keyword
- Reranking: Score results with a second model
- Query expansion: Generate multiple search queries
- Metadata filtering: Narrow by date, source, category
Generation Enhancement
Better use of retrieved context:
- Compression: Summarize long contexts
- Citation: Include source references
- Verification: Cross-check with multiple sources
- Iteration: Multi-step retrieval for complex questions
Self-Hosted RAG Solutions
Evaluation Metrics
Measure your RAG system's quality:
| Metric | What It Measures |
|---|---|
| Retrieval Precision | Relevance of retrieved docs |
| Retrieval Recall | Coverage of relevant docs |
| Answer Relevancy | How well answer addresses question |
| Faithfulness | Grounding in retrieved context |
| Context Precision | Efficiency of context usage |
Common Pitfalls
1. Wrong Chunk Size
Too small: Lost context Too large: Noise in retrieval
2. Poor Embedding Choice
Match embedding model to your domain.
3. Ignoring Metadata
Filter by date, source, or category when relevant.
4. No Reranking
Initial retrieval often benefits from a second pass.
5. Context Window Overflow
Don't retrieve more than the LLM can handle.
Production Considerations
- Caching: Cache frequent queries and embeddings
- Monitoring: Track retrieval quality over time
- Updates: Plan for document additions and changes
- Scaling: Consider distributed vector stores
- Security: Implement access controls for sensitive data
RAG vs Fine-tuning
| Aspect | RAG | Fine-tuning |
|---|---|---|
| Knowledge updates | Easy (re-index) | Requires retraining |
| Verifiability | Can cite sources | Black box |
| Cost | Lower ongoing | Higher upfront |
| Customization | Knowledge only | Behavior + knowledge |
| Hallucination | Reduced | Still possible |
Recommendation: Start with RAG. Add fine-tuning only if you need behavioral changes.
Conclusion
RAG has become essential for building AI applications that need custom knowledge. With tools like LlamaIndex, LangChain, and modern vector databases, you can create intelligent systems that provide accurate, grounded responses based on your own data.
Explore our RAG & Knowledge Management category to discover more tools for building retrieval-augmented applications.
